{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.7.13 |Anaconda 4.0.0 (x86_64)| (default, Dec 20 2016, 23:05:08) \n",
      "[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)]\n",
      "Numpy version: 1.11.2\n",
      "Pandas version: 0.19.2\n",
      "Matplotlib version: 1.5.3\n",
      "Seaborn version: 0.6.0\n"
     ]
    }
   ],
   "source": [
    "#Import relevant libraries and define settings for plotting.\n",
    "%matplotlib inline\n",
    "import sys\n",
    "print(sys.version)\n",
    "\n",
    "import numpy as np\n",
    "print 'Numpy version:', np.__version__\n",
    "\n",
    "import pandas as pd\n",
    "print 'Pandas version:', pd.__version__\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "print 'Matplotlib version:', mpl.__version__\n",
    "\n",
    "import seaborn as sns\n",
    "print 'Seaborn version:', sns.__version__\n",
    "\n",
    "import datetime\n",
    "import time\n",
    "\n",
    "sns.set()\n",
    "pal = sns.hls_palette(10, h=.5)\n",
    "sns.set_palette(pal)\n",
    "\n",
    "#Avoid display of scientific notation and show precision of 4 decimals:\n",
    "pd.set_option('display.float_format', lambda x: '%.4f' % x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Load complete dataset from 9/1/2014 to 9/1/2015 with ~31M entries. No data dictionary is available.\n",
    "#Source: http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml \n",
    "#direct source: !curl -O https://s3.amazonaws.com/nyc-tlc/misc/uber_nyc_data.csv\n",
    "df_uber = pd.read_csv('Data/uber_nyc_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30925738 entries, 0 to 30925737\n",
      "Data columns (total 6 columns):\n",
      "id                 int64\n",
      "origin_taz         object\n",
      "destination_taz    object\n",
      "pickup_datetime    object\n",
      "trip_distance      float64\n",
      "trip_duration      object\n",
      "dtypes: float64(1), int64(1), object(4)\n",
      "memory usage: 1.4+ GB\n"
     ]
    }
   ],
   "source": [
    "df_uber.info() #near 31 million entries, ~1.4GB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>origin_taz</th>\n",
       "      <th>destination_taz</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>252581</td>\n",
       "      <td>7C</td>\n",
       "      <td>6A</td>\n",
       "      <td>2014-09-01 09:00:00</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0:15:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>252582</td>\n",
       "      <td>7B</td>\n",
       "      <td>15</td>\n",
       "      <td>2014-09-01 18:00:00</td>\n",
       "      <td>10.17</td>\n",
       "      <td>0:34:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>252583</td>\n",
       "      <td>11</td>\n",
       "      <td>2A</td>\n",
       "      <td>2014-09-01 17:00:00</td>\n",
       "      <td>4.02</td>\n",
       "      <td>0:17:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>252584</td>\n",
       "      <td>3B</td>\n",
       "      <td>4A</td>\n",
       "      <td>2014-09-01 13:00:00</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0:06:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>252585</td>\n",
       "      <td>2A</td>\n",
       "      <td>10</td>\n",
       "      <td>2014-09-01 14:00:00</td>\n",
       "      <td>8.31</td>\n",
       "      <td>0:26:17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id origin_taz destination_taz      pickup_datetime  trip_distance  \\\n",
       "0  252581         7C              6A  2014-09-01 09:00:00           4.25   \n",
       "1  252582         7B              15  2014-09-01 18:00:00          10.17   \n",
       "2  252583         11              2A  2014-09-01 17:00:00           4.02   \n",
       "3  252584         3B              4A  2014-09-01 13:00:00           1.46   \n",
       "4  252585         2A              10  2014-09-01 14:00:00           8.31   \n",
       "\n",
       "  trip_duration  \n",
       "0       0:15:11  \n",
       "1       0:34:05  \n",
       "2       0:17:06  \n",
       "3       0:06:32  \n",
       "4       0:26:17  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_uber.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30925738"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The ID is unique\n",
    "len(df_uber.id.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check for missing or possibly erroneous data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There are no duplicated rows (this takes a while to run):\n",
    "len(df_uber[df_uber.duplicated() == True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                       0\n",
       "origin_taz               0\n",
       "destination_taz    1273023\n",
       "pickup_datetime          0\n",
       "trip_distance           38\n",
       "trip_duration           38\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show total null values per column. Use .any() in lieu of .sum() to just show which columns.\n",
    "df_uber.isnull().sum() #Note: ~1.3 million destination entries (4%) are null (missing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['7C', '7B', '11', '3B', '2A', '5B', '10', '2B', '9', '6B', '15',\n",
       "       '4C', '5A', '8', '14', '4A', '7A', '1', '16', '3A', '2C', '6A',\n",
       "       '12', '13', '3C', '4B', '5C', '17'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = df_uber.origin_taz.unique() #28 unique origin codes\n",
    "arr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['6A', '15', '2A', '4A', '10', '4C', '7A', '3C', '5B', '14', '8',\n",
       "       nan, '7C', '12', '2C', '1', '6B', '5C', '9', '3A', '2B', '11', '7B',\n",
       "       '5A', '13', '4B', '18', '16', '3B', '17'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = df_uber.destination_taz.unique() #30 unique destination codes, same as origin except for nan and 18.\n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{nan, '18'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(arr2) - set(arr1) #maybe code 18 represents EWR airport?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>origin_taz</th>\n",
       "      <th>destination_taz</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>252596</td>\n",
       "      <td>6B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-09-01 17:00:00</td>\n",
       "      <td>11.67</td>\n",
       "      <td>0:23:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>252613</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-09-01 16:00:00</td>\n",
       "      <td>80.69</td>\n",
       "      <td>1:59:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>252644</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-09-01 18:00:00</td>\n",
       "      <td>23.07</td>\n",
       "      <td>1:03:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>252722</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-09-01 10:00:00</td>\n",
       "      <td>21.73</td>\n",
       "      <td>0:41:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>252729</td>\n",
       "      <td>4B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-09-01 10:00:00</td>\n",
       "      <td>4.62</td>\n",
       "      <td>0:14:55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id origin_taz destination_taz      pickup_datetime  trip_distance  \\\n",
       "15   252596         6B             NaN  2014-09-01 17:00:00          11.67   \n",
       "32   252613          9             NaN  2014-09-01 16:00:00          80.69   \n",
       "63   252644          9             NaN  2014-09-01 18:00:00          23.07   \n",
       "141  252722          9             NaN  2014-09-01 10:00:00          21.73   \n",
       "148  252729         4B             NaN  2014-09-01 10:00:00           4.62   \n",
       "\n",
       "    trip_duration  \n",
       "15        0:23:45  \n",
       "32        1:59:37  \n",
       "63        1:03:37  \n",
       "141       0:41:12  \n",
       "148       0:14:55  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check some of the data with missing destination. \n",
    "#One idea to fill missing values would be predict destination based on the other features.\n",
    "df_uber[df_uber.destination_taz.isnull()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>origin_taz</th>\n",
       "      <th>destination_taz</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15155317</th>\n",
       "      <td>39535</td>\n",
       "      <td>2A</td>\n",
       "      <td>11</td>\n",
       "      <td>2015-04-25 12:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15245057</th>\n",
       "      <td>1009076</td>\n",
       "      <td>2A</td>\n",
       "      <td>2A</td>\n",
       "      <td>2015-04-26 01:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16519652</th>\n",
       "      <td>15028665</td>\n",
       "      <td>2A</td>\n",
       "      <td>7C</td>\n",
       "      <td>2015-04-29 21:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17148253</th>\n",
       "      <td>22250173</td>\n",
       "      <td>2A</td>\n",
       "      <td>2A</td>\n",
       "      <td>2015-04-12 02:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17297563</th>\n",
       "      <td>23716998</td>\n",
       "      <td>2C</td>\n",
       "      <td>11</td>\n",
       "      <td>2015-04-25 13:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                id origin_taz destination_taz      pickup_datetime  \\\n",
       "15155317     39535         2A              11  2015-04-25 12:00:00   \n",
       "15245057   1009076         2A              2A  2015-04-26 01:00:00   \n",
       "16519652  15028665         2A              7C  2015-04-29 21:00:00   \n",
       "17148253  22250173         2A              2A  2015-04-12 02:00:00   \n",
       "17297563  23716998         2C              11  2015-04-25 13:00:00   \n",
       "\n",
       "          trip_distance trip_duration  \n",
       "15155317            NaN           NaN  \n",
       "15245057            NaN           NaN  \n",
       "16519652            NaN           NaN  \n",
       "17148253            NaN           NaN  \n",
       "17297563            NaN           NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There are 38 records with values missing for trip distance and duration, none of which are missing the destination.\n",
    "df38 = df_uber[df_uber.trip_duration.isnull() & df_uber.trip_distance.isnull()]\n",
    "df38.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#All cases for which trip_duration is null match the cases for which trip_distance is also null:\n",
    "len(df_uber[(df_uber.trip_duration.isnull() == False) & (df_uber.trip_distance.isnull())])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse the pickup_datetime column to datetime type and create related features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#None of the pickup time data offer minutes or seconds precision\n",
    "len(df_uber[df_uber.pickup_datetime.apply(lambda x: x[-5:] == '00:00') == False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def dateParser(s):\n",
    "    \"\"\"\n",
    "    Function that takes a string in the format yyyy-mm-dd hh:mm:ss, and\n",
    "    returns the same as a datetime object.\n",
    "    \"\"\"\n",
    "    return datetime.datetime(int(s[0:4]), int(s[5:7]), int(s[8:10]), int(s[11:13]))\n",
    "\n",
    "#Other methods were tried, all slower (the slowest being parsing while reading file in Pandas):\n",
    "#return datetime.strptime(s, \"%Y-%m-%d %H:%M:%S.%f\")\n",
    "#return np.datetime64(s) equivalent to: np.array(df_uber.pickup_datetime[0]).astype(np.datetime64)\n",
    "#return pandas.Timestamp(s, \"%Y-%m-%d %H:%M:%S.%f\", tz='utc' )\n",
    "#source: http://stackoverflow.com/questions/11136006/python-pandas-what-is-the-fastest-way-to-create-a-datetime-index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.36902594566345"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Apply parser to data\n",
    "t0 = time.time()\n",
    "df_uber['pu_date_hour'] = df_uber.pickup_datetime.apply(dateParser)\n",
    "time.time() - t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014-09-01 00:00:00 2015-09-01 00:00:00 365 days 00:00:00\n"
     ]
    }
   ],
   "source": [
    "#Data encompasses exactly 365 days from Sept 1st, 2014 to Sep 1st, 2015.\n",
    "beginning = df_uber.pu_date_hour.min()\n",
    "end = df_uber.pu_date_hour.max()\n",
    "print beginning, end, end - beginning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Drop repeated column\n",
    "df_uber = df_uber.drop('pickup_datetime', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the federal holidays for the period (useful for analyzing data later)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-09-01                     Labor Day\n",
       "2014-10-13                  Columbus Day\n",
       "2014-11-11                  Veterans Day\n",
       "2014-11-27                  Thanksgiving\n",
       "2014-12-25                     Christmas\n",
       "2015-01-01                 New Years Day\n",
       "2015-01-19    Dr. Martin Luther King Jr.\n",
       "2015-02-16                Presidents Day\n",
       "2015-05-25                   MemorialDay\n",
       "2015-07-03                      July 4th\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get all federal holidays in the period\n",
    "from pandas.tseries.holiday import USFederalHolidayCalendar\n",
    "\n",
    "#if return_name is True, then name is series value and date is the series index, otherwise date is series value.\n",
    "holidays = USFederalHolidayCalendar().holidays(beginning, end, return_name = True) \n",
    "holidays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([u'Mon', u'Mon', u'Tue', u'Thu', u'Thu', u'Thu', u'Mon', u'Mon',\n",
       "       u'Mon', u'Fri'], \n",
       "      dtype='<U3')"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extract the weekday for each holiday\n",
    "holidays.index.map(lambda x: x.strftime('%a'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-08-31       Day Before Labor Day\n",
       "2014-09-01                  Labor Day\n",
       "2014-09-02        Day After Labor Day\n",
       "2014-10-12    Day Before Columbus Day\n",
       "2014-10-13               Columbus Day\n",
       "2014-10-14     Day After Columbus Day\n",
       "2014-11-10    Day Before Veterans Day\n",
       "2014-11-11               Veterans Day\n",
       "2014-11-12     Day After Veterans Day\n",
       "2014-11-26    Day Before Thanksgiving\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For completeness, let's add the day before and after the holidays (better: add day after if Thursday)\n",
    "holidays_all = pd.concat([holidays, \"Day After \" + holidays.shift(1, 'D'), \"Day Before \" + holidays.shift(-1, 'D')])\n",
    "holidays_all = holidays_all.sort_index()\n",
    "holidays_all.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create other date and time features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58.91848111152649"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a column with the date, without the time. This will be useful later.\n",
    "t0 = time.time()\n",
    "df_uber['pu_date'] = pd.Series(map(lambda x: x.astype('datetime64[D]'), df_uber['pu_date_hour'].values))\n",
    "#df_uber['pu_date'] = df_uber['pu_date_hour'].dt.date #this is way less memory effective and 11 sec slower to execute\n",
    "time.time() - t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Get month and year from pick up timestamp\n",
    "df_uber['year'] = df_uber['pu_date_hour'].dt.year\n",
    "df_uber['month'] = df_uber['pu_date_hour'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7083799839019775"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get trip pick up day of the month\n",
    "t0 = time.time()\n",
    "df_uber['day'] = df_uber['pu_date_hour'].dt.day #.apply(lambda x: x.day) = 105 seconds vs < 2 seconds with dt\n",
    "time.time() - t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.882817029953003"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get trip pick up hour from timestamp\n",
    "t0 = time.time()\n",
    "df_uber['hour'] = df_uber['pu_date_hour'].dt.hour #< 2 seconds vs 101 seconds using apply lambda\n",
    "time.time() - t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0246660709381104"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get trip pick up weekday from timestamp\n",
    "t0 = time.time()\n",
    "df_uber['weekday'] = df_uber['pu_date_hour'].dt.dayofweek\n",
    "time.time() - t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    5142832\n",
       "4    4805002\n",
       "3    4625511\n",
       "6    4331195\n",
       "2    4301537\n",
       "1    4010434\n",
       "0    3709227\n",
       "Name: weekday, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_uber.weekday.value_counts() #Monday = 0, Sunday = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>origin_taz</th>\n",
       "      <th>destination_taz</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>pu_date_hour</th>\n",
       "      <th>pu_date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>252581</td>\n",
       "      <td>7C</td>\n",
       "      <td>6A</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0:15:11</td>\n",
       "      <td>2014-09-01 09:00:00</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>252582</td>\n",
       "      <td>7B</td>\n",
       "      <td>15</td>\n",
       "      <td>10.17</td>\n",
       "      <td>0:34:05</td>\n",
       "      <td>2014-09-01 18:00:00</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>252583</td>\n",
       "      <td>11</td>\n",
       "      <td>2A</td>\n",
       "      <td>4.02</td>\n",
       "      <td>0:17:06</td>\n",
       "      <td>2014-09-01 17:00:00</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>252584</td>\n",
       "      <td>3B</td>\n",
       "      <td>4A</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0:06:32</td>\n",
       "      <td>2014-09-01 13:00:00</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>252585</td>\n",
       "      <td>2A</td>\n",
       "      <td>10</td>\n",
       "      <td>8.31</td>\n",
       "      <td>0:26:17</td>\n",
       "      <td>2014-09-01 14:00:00</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id origin_taz destination_taz  trip_distance trip_duration  \\\n",
       "0  252581         7C              6A           4.25       0:15:11   \n",
       "1  252582         7B              15          10.17       0:34:05   \n",
       "2  252583         11              2A           4.02       0:17:06   \n",
       "3  252584         3B              4A           1.46       0:06:32   \n",
       "4  252585         2A              10           8.31       0:26:17   \n",
       "\n",
       "         pu_date_hour    pu_date  year  month  day  hour  weekday  \n",
       "0 2014-09-01 09:00:00 2014-09-01  2014      9    1     9        0  \n",
       "1 2014-09-01 18:00:00 2014-09-01  2014      9    1    18        0  \n",
       "2 2014-09-01 17:00:00 2014-09-01  2014      9    1    17        0  \n",
       "3 2014-09-01 13:00:00 2014-09-01  2014      9    1    13        0  \n",
       "4 2014-09-01 14:00:00 2014-09-01  2014      9    1    14        0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_uber.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30925738 entries, 0 to 30925737\n",
      "Data columns (total 12 columns):\n",
      "id                 int64\n",
      "origin_taz         object\n",
      "destination_taz    object\n",
      "trip_distance      float64\n",
      "trip_duration      object\n",
      "pu_date_hour       datetime64[ns]\n",
      "pu_date            datetime64[ns]\n",
      "year               int64\n",
      "month              int64\n",
      "day                int64\n",
      "hour               int64\n",
      "weekday            int64\n",
      "dtypes: datetime64[ns](2), float64(1), int64(6), object(3)\n",
      "memory usage: 2.8+ GB\n"
     ]
    }
   ],
   "source": [
    "df_uber.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform trip duration data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "uniq_dur = df_uber[df_uber.trip_duration.isnull() == False].trip_duration.unique() #19393"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "386\n"
     ]
    }
   ],
   "source": [
    "#Among unique duration strings, find how many represent >= 10h of duration:\n",
    "long_duration = [] #>= 10 hours or 600 minutes\n",
    "for item in uniq_dur:\n",
    "    if len(item) != 7:\n",
    "        long_duration.append(item)\n",
    "\n",
    "#long_duration\n",
    "print len(long_duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "172032:14:50\n",
      "172032:39:03\n",
      "136:25:12\n",
      "172032:17:45\n",
      "172032:19:27\n",
      "119:53:35\n",
      "120:49:05\n"
     ]
    }
   ],
   "source": [
    "#Check for the most unusual strings for trip duration: some erroneous entries need to be addressed\n",
    "for item in long_duration:\n",
    "    if len(item) > 8:\n",
    "        print item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def duration_to_minutes(s):\n",
    "    \"\"\"\n",
    "    Function that takes a string with the hh:mm:ss format and\n",
    "    returns the integer equivalent of the total time in minutes, \n",
    "    or zero for missing values in a Pandas dataframe.\n",
    "    \"\"\"\n",
    "    if pd.isnull(s):\n",
    "        val = 0 #note: this fills with 0 the 38 instances with null (missing) values\n",
    "    else:\n",
    "        hms = s.split(':')\n",
    "        val = int(hms[0])*60 + int(hms[1]) + int(hms[2])/60.0\n",
    "    return val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88.3189799785614"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Transform the trip duration string into trip duration in minutes:\n",
    "t0 = time.time()\n",
    "df_uber['duration_min'] = df_uber.trip_duration.apply(duration_to_minutes) \n",
    "time.time() - t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>origin_taz</th>\n",
       "      <th>destination_taz</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>pu_date_hour</th>\n",
       "      <th>pu_date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>duration_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>252581</td>\n",
       "      <td>7C</td>\n",
       "      <td>6A</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0:15:11</td>\n",
       "      <td>2014-09-01 09:00:00</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>15.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>252582</td>\n",
       "      <td>7B</td>\n",
       "      <td>15</td>\n",
       "      <td>10.17</td>\n",
       "      <td>0:34:05</td>\n",
       "      <td>2014-09-01 18:00:00</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>34.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>252583</td>\n",
       "      <td>11</td>\n",
       "      <td>2A</td>\n",
       "      <td>4.02</td>\n",
       "      <td>0:17:06</td>\n",
       "      <td>2014-09-01 17:00:00</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>17.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>252584</td>\n",
       "      <td>3B</td>\n",
       "      <td>4A</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0:06:32</td>\n",
       "      <td>2014-09-01 13:00:00</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>252585</td>\n",
       "      <td>2A</td>\n",
       "      <td>10</td>\n",
       "      <td>8.31</td>\n",
       "      <td>0:26:17</td>\n",
       "      <td>2014-09-01 14:00:00</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>26.283333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id origin_taz destination_taz  trip_distance trip_duration  \\\n",
       "0  252581         7C              6A           4.25       0:15:11   \n",
       "1  252582         7B              15          10.17       0:34:05   \n",
       "2  252583         11              2A           4.02       0:17:06   \n",
       "3  252584         3B              4A           1.46       0:06:32   \n",
       "4  252585         2A              10           8.31       0:26:17   \n",
       "\n",
       "         pu_date_hour    pu_date  year  month  day  hour  weekday  \\\n",
       "0 2014-09-01 09:00:00 2014-09-01  2014      9    1     9        0   \n",
       "1 2014-09-01 18:00:00 2014-09-01  2014      9    1    18        0   \n",
       "2 2014-09-01 17:00:00 2014-09-01  2014      9    1    17        0   \n",
       "3 2014-09-01 13:00:00 2014-09-01  2014      9    1    13        0   \n",
       "4 2014-09-01 14:00:00 2014-09-01  2014      9    1    14        0   \n",
       "\n",
       "   duration_min  \n",
       "0     15.183333  \n",
       "1     34.083333  \n",
       "2     17.100000  \n",
       "3      6.533333  \n",
       "4     26.283333  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_uber.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Replace missing values with mean distance and duration for respective origin-destination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Get the mean distance and duration for each origin-destination pair\n",
    "df_DistDur = df_uber.groupby(['origin_taz', 'destination_taz'])[['trip_distance', 'duration_min']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>duration_min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin_taz</th>\n",
       "      <th>destination_taz</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>1.421855</td>\n",
       "      <td>10.565513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.650791</td>\n",
       "      <td>17.977578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.093679</td>\n",
       "      <td>22.489925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8.887789</td>\n",
       "      <td>29.102406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.583137</td>\n",
       "      <td>32.156759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            trip_distance  duration_min\n",
       "origin_taz destination_taz                             \n",
       "1          1                     1.421855     10.565513\n",
       "           10                    3.650791     17.977578\n",
       "           11                    5.093679     22.489925\n",
       "           12                    8.887789     29.102406\n",
       "           13                   11.583137     32.156759"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_DistDur.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Replace 38 missing values with the average distance and duration for the respective origin-destination pair\n",
    "for i in df38.index:\n",
    "    orig = df_uber.loc[i, 'origin_taz']\n",
    "    dest = df_uber.loc[i, 'destination_taz']\n",
    "    df_uber.loc[i, 'trip_distance'] = df_DistDur.loc[orig, dest].trip_distance\n",
    "    df_uber.loc[i, 'duration_min'] = df_DistDur.loc[orig, dest].duration_min"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate average speed for each trip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Calculate average trip speed (mph) to help in understanding outliers (like trips with duration >10 h):\n",
    "df_uber['trip_mph_avg'] = df_uber.trip_distance/(df_uber.duration_min/60.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>origin_taz</th>\n",
       "      <th>destination_taz</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>pu_date_hour</th>\n",
       "      <th>pu_date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>trip_mph_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15155317</th>\n",
       "      <td>39535</td>\n",
       "      <td>2A</td>\n",
       "      <td>11</td>\n",
       "      <td>3.936804</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-04-25 12:00:00</td>\n",
       "      <td>2015-04-25</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>19.732101</td>\n",
       "      <td>11.970759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15245057</th>\n",
       "      <td>1009076</td>\n",
       "      <td>2A</td>\n",
       "      <td>2A</td>\n",
       "      <td>1.358751</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-04-26 01:00:00</td>\n",
       "      <td>2015-04-26</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>10.919522</td>\n",
       "      <td>7.465989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16519652</th>\n",
       "      <td>15028665</td>\n",
       "      <td>2A</td>\n",
       "      <td>7C</td>\n",
       "      <td>9.039374</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-04-29 21:00:00</td>\n",
       "      <td>2015-04-29</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>30.204064</td>\n",
       "      <td>17.956604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17148253</th>\n",
       "      <td>22250173</td>\n",
       "      <td>2A</td>\n",
       "      <td>2A</td>\n",
       "      <td>1.358751</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-04-12 02:00:00</td>\n",
       "      <td>2015-04-12</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>10.919522</td>\n",
       "      <td>7.465989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17297563</th>\n",
       "      <td>23716998</td>\n",
       "      <td>2C</td>\n",
       "      <td>11</td>\n",
       "      <td>4.833061</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-04-25 13:00:00</td>\n",
       "      <td>2015-04-25</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>24.385143</td>\n",
       "      <td>11.891817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                id origin_taz destination_taz  trip_distance trip_duration  \\\n",
       "15155317     39535         2A              11       3.936804           NaN   \n",
       "15245057   1009076         2A              2A       1.358751           NaN   \n",
       "16519652  15028665         2A              7C       9.039374           NaN   \n",
       "17148253  22250173         2A              2A       1.358751           NaN   \n",
       "17297563  23716998         2C              11       4.833061           NaN   \n",
       "\n",
       "                pu_date_hour    pu_date  year  month  day  hour  weekday  \\\n",
       "15155317 2015-04-25 12:00:00 2015-04-25  2015      4   25    12        5   \n",
       "15245057 2015-04-26 01:00:00 2015-04-26  2015      4   26     1        6   \n",
       "16519652 2015-04-29 21:00:00 2015-04-29  2015      4   29    21        2   \n",
       "17148253 2015-04-12 02:00:00 2015-04-12  2015      4   12     2        6   \n",
       "17297563 2015-04-25 13:00:00 2015-04-25  2015      4   25    13        5   \n",
       "\n",
       "          duration_min  trip_mph_avg  \n",
       "15155317     19.732101     11.970759  \n",
       "15245057     10.919522      7.465989  \n",
       "16519652     30.204064     17.956604  \n",
       "17148253     10.919522      7.465989  \n",
       "17297563     24.385143     11.891817  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check that trip_distance and duration_min have been replaced, and trip speed has been calculated:\n",
    "df_uber.iloc[df38.index, :].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Drop redundant trip_duration columns\n",
    "df_uber = df_uber.drop('trip_duration', axis=1)\n",
    "df_uber = df_uber.drop('pu_date_hour', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Estimated Revenue per Trip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Source: http://uberestimate.com/prices/New-York-City/ for Uber X\n",
    "base_fare = 2.55\n",
    "per_minute = 0.35\n",
    "per_mile = 1.75\n",
    "min_fare = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#When not possible to work with Pandas dataframe directly (due to memory usage), go back to array!\n",
    "#test = df_uber[['duration_min', 'trip_distance']].values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#def est_revenue(arr):\n",
    "#    rev = base_fare + arr[0] * per_minute + arr[1] * per_mile\n",
    "#    return rev if rev > min_fare else min_fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#df_uber['est_revenue'] = pd.Series(map(lambda x: est_revenue(x), test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Using eval() is even more efficient for large datasets than resorting to Numpy! \n",
    "#Local variables must be followed by @. Only for arithmetic, cannot use if statement, for example.\n",
    "df_uber['est_revenue'] = df_uber.eval('@base_fare + duration_min * @per_minute + trip_distance * @per_mile')\n",
    "df_uber.loc[df_uber.est_revenue < 8, 'est_revenue'] = min_fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>origin_taz</th>\n",
       "      <th>destination_taz</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>pu_date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>trip_mph_avg</th>\n",
       "      <th>est_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>252581</td>\n",
       "      <td>7C</td>\n",
       "      <td>6A</td>\n",
       "      <td>4.25</td>\n",
       "      <td>2014-09-01</td>\n",
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       "      <td>9</td>\n",
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       "      <td>16.794731</td>\n",
       "      <td>15.301667</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>252582</td>\n",
       "      <td>7B</td>\n",
       "      <td>15</td>\n",
       "      <td>10.17</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>34.083333</td>\n",
       "      <td>17.903178</td>\n",
       "      <td>32.276667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>252583</td>\n",
       "      <td>11</td>\n",
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       "      <td>4.02</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>14.105263</td>\n",
       "      <td>15.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>252584</td>\n",
       "      <td>3B</td>\n",
       "      <td>4A</td>\n",
       "      <td>1.46</td>\n",
       "      <td>2014-09-01</td>\n",
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       "      <td>6.533333</td>\n",
       "      <td>13.408163</td>\n",
       "      <td>8.000000</td>\n",
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       "      <th>4</th>\n",
       "      <td>252585</td>\n",
       "      <td>2A</td>\n",
       "      <td>10</td>\n",
       "      <td>8.31</td>\n",
       "      <td>2014-09-01</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>26.283333</td>\n",
       "      <td>18.970197</td>\n",
       "      <td>26.291667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id origin_taz destination_taz  trip_distance    pu_date  year  month  \\\n",
       "0  252581         7C              6A           4.25 2014-09-01  2014      9   \n",
       "1  252582         7B              15          10.17 2014-09-01  2014      9   \n",
       "2  252583         11              2A           4.02 2014-09-01  2014      9   \n",
       "3  252584         3B              4A           1.46 2014-09-01  2014      9   \n",
       "4  252585         2A              10           8.31 2014-09-01  2014      9   \n",
       "\n",
       "   day  hour  weekday  duration_min  trip_mph_avg  est_revenue  \n",
       "0    1     9        0     15.183333     16.794731    15.301667  \n",
       "1    1    18        0     34.083333     17.903178    32.276667  \n",
       "2    1    17        0     17.100000     14.105263    15.570000  \n",
       "3    1    13        0      6.533333     13.408163     8.000000  \n",
       "4    1    14        0     26.283333     18.970197    26.291667  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_uber.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Queries for Checking Data Consistency."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A closer look of the anomalous cases of trip duration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "78069.74916666662"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for very long duration entries, and effect on revenue numbers:\n",
    "\n",
    "#>=100h:\n",
    "print len(df_uber[(df_uber.duration_min >= 6000)]) #7\n",
    "\n",
    "#Greater than 100h: 7 entries (erroneous): system error? fraud? Total revenue is relevant: $14,459,978 (2.4% of total)\n",
    "#172032:14:50 #probable system error: distance < 10 miles\n",
    "#172032:39:03 #probable system error: distance < 10 miles\n",
    "#136:25:12 #short distance\n",
    "#172032:17:45 #probable system error: distance < 10 miles\n",
    "#172032:19:27 #probable system error: distance < 10 miles\n",
    "#119:53:35 #long distance, incorrect destination?\n",
    "#120:49:05 #long distance, this entry has missing destination\n",
    "\n",
    "#>16h and <100h : 116 entries\n",
    "df_uber[(df_uber.duration_min > 960) & (df_uber.duration_min < 6000)].est_revenue.sum() #$78,070 total revenue\n",
    "\n",
    "#The effect on revenue is relevant only for the trips higher than 100h of duration, which are most likely a result of \n",
    "#some system error (as seen above), or perhaps fraud, therefore these 7 entries will be excluded from the dataset for\n",
    "#analysis. However, assuming that driving more than 16h (960 minutes) non-stop per day represents unreliable data, \n",
    "#these additional 116 cases will also be excluded from analysis. These 123 data points warrant more in-depth internal\n",
    "#investigation to bring light to what may have caused them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24866\n"
     ]
    },
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       "      <th>21847</th>\n",
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       "      <td>4C</td>\n",
       "      <td>2A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-09-29</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21909</th>\n",
       "      <td>274490</td>\n",
       "      <td>5C</td>\n",
       "      <td>6A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-09-29</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22179</th>\n",
       "      <td>274760</td>\n",
       "      <td>11</td>\n",
       "      <td>4A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-09-29</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22359</th>\n",
       "      <td>274940</td>\n",
       "      <td>3C</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id origin_taz destination_taz  trip_distance    pu_date  year  \\\n",
       "21726  274307         4B               8            0.0 2014-09-29  2014   \n",
       "21847  274428         4C              2A            0.0 2014-09-29  2014   \n",
       "21909  274490         5C              6A            0.0 2014-09-29  2014   \n",
       "22179  274760         11              4A            0.0 2014-09-29  2014   \n",
       "22359  274940         3C               1            0.0 2014-09-30  2014   \n",
       "\n",
       "       month  day  hour  weekday  duration_min  trip_mph_avg  est_revenue  \n",
       "21726      9   29    17        0           0.0           NaN          8.0  \n",
       "21847      9   29    17        0           0.0           NaN          8.0  \n",
       "21909      9   29    19        0           0.0           NaN          8.0  \n",
       "22179      9   29    17        0           0.0           NaN          8.0  \n",
       "22359      9   30    14        1           0.0           NaN          8.0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Were the trips with distance and duration equal to zero actually cancelled (didn't happen)? \n",
    "\n",
    "print len(df_uber[(df_uber.duration_min == 0) & (df_uber.trip_distance == 0)]) #24866\n",
    "#There is at least one case almost daily. The 24866 cases represent a revenue of $198,928, based on $8 minimum fare.\n",
    "\n",
    "#Note that origin and destination are the same, except for 910 trips: did these trips in fact occur?\n",
    "df_uber[(df_uber.duration_min == 0) & (df_uber.trip_distance == 0) & (df_uber.origin_taz != df_uber.destination_taz)].\\\n",
    "head()#910\n",
    "\n",
    "#There is generally a fee associated with trip cancellation, so unless these trips represent a system error or fraud,\n",
    "#there was no loss of the minimum fare revenue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85515\n"
     ]
    },
    {
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      "text/plain": [
       "                id origin_taz destination_taz  trip_distance    pu_date  year  \\\n",
       "882890    18153735          1              2A            0.0 2014-09-28  2014   \n",
       "3223424   29754237         4C              6A            0.0 2014-10-05  2014   \n",
       "18154210   2023861          1              4C            0.0 2015-05-28  2015   \n",
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       "21517547   4482860          1              5C            0.0 2015-06-23  2015   \n",
       "21727436   6421520         6B             NaN            0.0 2015-06-26  2015   \n",
       "\n",
       "          month  day  hour  weekday  duration_min  trip_mph_avg  est_revenue  \n",
       "882890        9   28     1        6      7.116667           0.0     8.000000  \n",
       "3223424      10    5    20        6      7.516667           0.0     8.000000  \n",
       "18154210      5   28    12        3     27.200000           0.0    12.070000  \n",
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       "20674992      5   28    11        3     32.850000           0.0    14.047500  \n",
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       "21727436      6   26     9        4     78.233333           0.0    29.931667  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check cases with distance equal to zero but duration greater than zero:\n",
    "\n",
    "print len(df_uber[(df_uber.duration_min > 0) & (df_uber.trip_distance == 0)]) #85515\n",
    "\n",
    "#The median duration for trips with zero distance is 10 seconds (mean= 2.4 minutes), so most of these 85,515 cases \n",
    "#possibly represent trips that were cancelled right after they were registered.\n",
    "\n",
    "#3873 trips of the 85,515 cases show pick up and drop off in different taxi zones \n",
    "#(median duration: 10 seconds, mean: 4.5 minutes).\n",
    "\n",
    "#Let's check the small dataset (277) out of the 3873 cases with duration > 5 minutes, but distance equal to zero:\n",
    "#Maybe these cases represent some error with registering the distance traveled?\n",
    "df_uber[(df_uber.duration_min >= 5) & (df_uber.trip_distance == 0) &(df_uber.origin_taz != df_uber.destination_taz)]\\\n",
    ".head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A closer look of the anomalous cases for the calculated average trip speed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "262666\n"
     ]
    },
    {
     "data": {
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       "      <td>2014-09-02</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id origin_taz destination_taz  trip_distance    pu_date  year  \\\n",
       "82    252663         14              14           0.02 2014-09-01  2014   \n",
       "120   252701         2A              2A           1.51 2014-09-01  2014   \n",
       "271   252852          8               8           0.00 2014-09-01  2014   \n",
       "542   253123         4C              4C           0.05 2014-09-02  2014   \n",
       "554   253135         2A              2A           0.00 2014-09-02  2014   \n",
       "605   253186         2A              2A           0.05 2014-09-02  2014   \n",
       "839   253420         2A              2A           0.47 2014-09-02  2014   \n",
       "896   253477         5C              6B           0.99 2014-09-02  2014   \n",
       "945   253526         14              14           0.04 2014-09-02  2014   \n",
       "1260  253841         4A              4A           0.06 2014-09-02  2014   \n",
       "\n",
       "      month  day  hour  weekday  duration_min  trip_mph_avg  est_revenue  \n",
       "82        9    1    11        0      1.333333      0.900000     8.000000  \n",
       "120       9    1    20        0     33.883333      2.673881    17.051667  \n",
       "271       9    1    16        0      0.500000      0.000000     8.000000  \n",
       "542       9    2    17        1      1.100000      2.727273     8.000000  \n",
       "554       9    2     9        1      2.683333      0.000000     8.000000  \n",
       "605       9    2    20        1      1.450000      2.068966     8.000000  \n",
       "839       9    2    13        1     10.583333      2.664567     8.000000  \n",
       "896       9    2    17        1     22.183333      2.677686    12.046667  \n",
       "945       9    2     2        1      1.400000      1.714286     8.000000  \n",
       "1260      9    2    20        1      1.350000      2.666667     8.000000  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the trips with average speed slower than walking (3mph).\n",
    "\n",
    "print len(df_uber[df_uber.trip_mph_avg <= 3]) #262,666 cases\n",
    "\n",
    "#Considering that some really bad traffic is possible, let's check the proportion of cases that fall under \n",
    "#2 miles or less traveled distance:\n",
    "df_uber[(df_uber.trip_mph_avg <= 3) & (df_uber.trip_distance <= 2)].head(10) #246,225, or ~94% of all trips with < 3mph\n",
    "\n",
    "#0.8% of cases from the entire dataset have calculated speed < 3mph : really bad traffic cases, interrupted trips, \n",
    "#or fraud? We will assume they're mostly really bad traffic cases or interrupted trips for this analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>origin_taz</th>\n",
       "      <th>destination_taz</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>pu_date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>trip_mph_avg</th>\n",
       "      <th>est_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>253120</td>\n",
       "      <td>5B</td>\n",
       "      <td>10</td>\n",
       "      <td>8.34</td>\n",
       "      <td>2014-09-02</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>173.366667</td>\n",
       "      <td>2.886368</td>\n",
       "      <td>77.823333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7798</th>\n",
       "      <td>260379</td>\n",
       "      <td>6A</td>\n",
       "      <td>6A</td>\n",
       "      <td>6.01</td>\n",
       "      <td>2014-09-11</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>128.150000</td>\n",
       "      <td>2.813890</td>\n",
       "      <td>57.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14734</th>\n",
       "      <td>267315</td>\n",
       "      <td>6B</td>\n",
       "      <td>5C</td>\n",
       "      <td>9.08</td>\n",
       "      <td>2014-09-19</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>196.250000</td>\n",
       "      <td>2.776051</td>\n",
       "      <td>87.127500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>267577</td>\n",
       "      <td>4C</td>\n",
       "      <td>6A</td>\n",
       "      <td>5.48</td>\n",
       "      <td>2014-09-20</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>110.700000</td>\n",
       "      <td>2.970190</td>\n",
       "      <td>50.885000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17131</th>\n",
       "      <td>269712</td>\n",
       "      <td>2A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.88</td>\n",
       "      <td>2014-09-21</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>100.466667</td>\n",
       "      <td>2.914399</td>\n",
       "      <td>46.253333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21955</th>\n",
       "      <td>274536</td>\n",
       "      <td>6B</td>\n",
       "      <td>6B</td>\n",
       "      <td>2.97</td>\n",
       "      <td>2014-09-29</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>91.083333</td>\n",
       "      <td>1.956450</td>\n",
       "      <td>39.626667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22567</th>\n",
       "      <td>275148</td>\n",
       "      <td>4C</td>\n",
       "      <td>4C</td>\n",
       "      <td>1.37</td>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>106.483333</td>\n",
       "      <td>0.771952</td>\n",
       "      <td>42.216667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24080</th>\n",
       "      <td>736706</td>\n",
       "      <td>6B</td>\n",
       "      <td>3A</td>\n",
       "      <td>6.05</td>\n",
       "      <td>2014-09-02</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>243.216667</td>\n",
       "      <td>1.492496</td>\n",
       "      <td>98.263333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27977</th>\n",
       "      <td>740603</td>\n",
       "      <td>4C</td>\n",
       "      <td>2B</td>\n",
       "      <td>4.99</td>\n",
       "      <td>2014-09-07</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>129.950000</td>\n",
       "      <td>2.303963</td>\n",
       "      <td>56.765000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29077</th>\n",
       "      <td>741703</td>\n",
       "      <td>4A</td>\n",
       "      <td>5A</td>\n",
       "      <td>7.94</td>\n",
       "      <td>2014-09-08</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>208.450000</td>\n",
       "      <td>2.285440</td>\n",
       "      <td>89.402500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id origin_taz destination_taz  trip_distance    pu_date  year  \\\n",
       "539    253120         5B              10           8.34 2014-09-02  2014   \n",
       "7798   260379         6A              6A           6.01 2014-09-11  2014   \n",
       "14734  267315         6B              5C           9.08 2014-09-19  2014   \n",
       "14996  267577         4C              6A           5.48 2014-09-20  2014   \n",
       "17131  269712         2A             NaN           4.88 2014-09-21  2014   \n",
       "21955  274536         6B              6B           2.97 2014-09-29  2014   \n",
       "22567  275148         4C              4C           1.37 2014-09-30  2014   \n",
       "24080  736706         6B              3A           6.05 2014-09-02  2014   \n",
       "27977  740603         4C              2B           4.99 2014-09-07  2014   \n",
       "29077  741703         4A              5A           7.94 2014-09-08  2014   \n",
       "\n",
       "       month  day  hour  weekday  duration_min  trip_mph_avg  est_revenue  \n",
       "539        9    2    11        1    173.366667      2.886368    77.823333  \n",
       "7798       9   11    12        3    128.150000      2.813890    57.920000  \n",
       "14734      9   19    13        4    196.250000      2.776051    87.127500  \n",
       "14996      9   20    15        5    110.700000      2.970190    50.885000  \n",
       "17131      9   21    20        6    100.466667      2.914399    46.253333  \n",
       "21955      9   29    12        0     91.083333      1.956450    39.626667  \n",
       "22567      9   30    14        1    106.483333      0.771952    42.216667  \n",
       "24080      9    2    16        1    243.216667      1.492496    98.263333  \n",
       "27977      9    7    18        6    129.950000      2.303963    56.765000  \n",
       "29077      9    8    15        0    208.450000      2.285440    89.402500  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Among the trips with very slow speed (< 3 mph), the most suspicious cases are perhaps those that show very long\n",
    "#duration. Let's say that 1.5h would have been enough, in most situations, to get outside of some traffic gridlock, \n",
    "#then part of these cases should be investigated more in depth, especially because they represent a significant \n",
    "#share of the revenue ($850K).\n",
    "\n",
    "df_uber[(df_uber.trip_mph_avg <= 3) & (df_uber.duration_min > 90) & (df_uber.duration_min <= 960)].head(10)#8393 cases\n",
    "\n",
    "#Due to the lack of specific domain knowledge about the system that has generated this data, and unavailability of \n",
    "#more detailed trip information, these cases will be disregarded as suspicious in this analysis. It's possible, \n",
    "#for example, that some of the cases represent a driver waiting for a passenger during a trip, even though this is \n",
    "#not a typical behaviour in ride sharing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3368404"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_viz[(df_viz.month==8)&(df_viz.year==2015)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter Dataset, Define Functions, and Load Methods to be Used to Create Visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1852"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#A few data points represent the zeroth hour of Sep, 2015, extending the time period beyond exactly 365 days:\n",
    "len(df_uber[df_uber.pu_date == datetime.date(2015, 9, 1)]) #1852 data points to be censored for convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create dataframe to be used for visualization with exactly 365 days of data, and max trip duration of 16h:\n",
    "df_viz = df_uber[(df_uber.pu_date != datetime.date(2015, 9, 1)) & (df_uber.duration_min <= 960)].copy() #1975 cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_min</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>trip_mph_avg</th>\n",
       "      <th>est_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30923763.0000</td>\n",
       "      <td>30923763.0000</td>\n",
       "      <td>30898897.0000</td>\n",
       "      <td>30923763.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20.9923</td>\n",
       "      <td>5.1996</td>\n",
       "      <td>13.5071</td>\n",
       "      <td>19.2419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.0846</td>\n",
       "      <td>6.1308</td>\n",
       "      <td>11.7569</td>\n",
       "      <td>15.0809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.8500</td>\n",
       "      <td>1.6900</td>\n",
       "      <td>8.4098</td>\n",
       "      <td>9.5733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.2333</td>\n",
       "      <td>3.0900</td>\n",
       "      <td>11.4488</td>\n",
       "      <td>14.3508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>26.6833</td>\n",
       "      <td>6.2300</td>\n",
       "      <td>16.2667</td>\n",
       "      <td>23.0308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>959.3000</td>\n",
       "      <td>568.5700</td>\n",
       "      <td>22548.0000</td>\n",
       "      <td>1225.8583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       duration_min  trip_distance  trip_mph_avg   est_revenue\n",
       "count 30923763.0000  30923763.0000 30898897.0000 30923763.0000\n",
       "mean        20.9923         5.1996       13.5071       19.2419\n",
       "std         16.0846         6.1308       11.7569       15.0809\n",
       "min          0.0000         0.0000        0.0000        8.0000\n",
       "25%         10.8500         1.6900        8.4098        9.5733\n",
       "50%         17.2333         3.0900       11.4488       14.3508\n",
       "75%         26.6833         6.2300       16.2667       23.0308\n",
       "max        959.3000       568.5700    22548.0000     1225.8583"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Descriptive statistics for numerical features:\n",
    "df_viz[['duration_min', 'trip_distance', 'trip_mph_avg', 'est_revenue']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Required by Plotly:\n",
    "import matplotlib.mlab as mlab\n",
    "import plotly.plotly as py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.ticker import FuncFormatter #Call formatter function to format tick values\n",
    "from matplotlib.offsetbox import (OffsetImage, AnnotationBbox) #Create image box\n",
    "from matplotlib._png import read_png #Load png file\n",
    "from matplotlib.patches import Ellipse #Draw ellipse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create functions to format tick numbers\n",
    "def thousands_comma(x, pos):\n",
    "    \"\"\"\n",
    "    Args are the value and tick position. \n",
    "    Returns number with thousands comma and no decimals.\n",
    "    \"\"\"\n",
    "    return '{:,.0f}'.format(x) #this is the new syntax for formatting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def thousands_format(x, pos):\n",
    "    \"\"\"\n",
    "    Args are the value and tick position. \n",
    "    Returns number of thousands with one decimal, and K in lieu of 3 zeros.\n",
    "    \"\"\"\n",
    "    return '{:.0f}{}'.format(x * 1e-3, 'K') #old syntax: '%1.0fK' % (x * 1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def millions_format(x, pos):\n",
    "    \"\"\"\n",
    "    Args are the value and tick position. \n",
    "    Returns number of millions with one decimal, and M in lieu of 6 zeros.\n",
    "    \"\"\"\n",
    "    return '{:.1f}{}'.format(x * 1e-6, 'M') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def millions_currency(x, pos):\n",
    "    \"\"\"\n",
    "    Args are the value and tick position. \n",
    "    Returns number of millions with a $ sign, M in lieu of 6 zeros, and no decimals.\n",
    "    \"\"\"\n",
    "    return '{}{:.0f}{}'.format('$', x * 1e-6, 'M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Define robust function to automatically add annotated labels on bar plots.\n",
    "#Inspiration from http://composition.al/blog/2015/11/29/a-better-way-to-add-labels-to-bar-charts-with-matplotlib/\n",
    "\n",
    "def annotate_labels(ax, labels_list, **kwargs):\n",
    "    \"\"\"\n",
    "    Function to automatically add labels on bar charts.\n",
    "    It takes a plot axis, an ordered list of labels, and text kwargs.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Get y-axis height to calculate label position from.\n",
    "    (y_bottom, y_top) = ax.get_ylim()\n",
    "    y_height = y_top - y_bottom\n",
    "    \n",
    "    rects = ax.patches\n",
    "\n",
    "    for rect, label in zip(rects, labels_list):\n",
    "        height = rect.get_height()\n",
    "        p_height = (height / y_height) # Fraction of axis height taken up by this rectangle\n",
    "        label_position = height + (y_height * 0.01)\n",
    "        \n",
    "        # If we can fit the label above the column, do that;\n",
    "        # otherwise, put it inside the column (or else, change ylim)\n",
    "        #if p_height > 0.95:\n",
    "        #    label_position = height - (y_height * 0.05)\n",
    "        #else:\n",
    "        #    label_position = height + (y_height * 0.01)\n",
    "\n",
    "        ax.text(rect.get_x() + rect.get_width()/2., label_position, label, kwargs)\n",
    "    return None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the demand: number of Uber trips per day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pu_date\n",
       "2014-09-01    31592\n",
       "2014-09-02    41496\n",
       "2014-09-03    46339\n",
       "2014-09-04    53402\n",
       "2014-09-05    59718\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a plotting dataframe with counts (number of trips) grouped by day:\n",
    "byDate = df_viz.groupby('pu_date')['id'].count() #365 complete entries\n",
    "byDate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pu_date\n",
       "2014-10-28     52084\n",
       "2014-10-29     61082\n",
       "2014-10-30     66369\n",
       "2014-10-31     71787\n",
       "2014-11-01    104450\n",
       "2014-11-02     57289\n",
       "2014-11-03     50491\n",
       "2014-11-04     52395\n",
       "2014-11-05     61518\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byDate['2014-10-28': '2014-11-05'] #an uptick in rides on 11/1, the day before the NYC marathon!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "107630.82608695653"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byDate['2015-06-01': '2015-08-31'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autumn-48.png         Snow Storm-48.png     Winter-48.png\r\n",
      "Christmas Tree-48.png Snow-48.png           marathon.png\r\n",
      "Leave-48.png          Spring-50.png         sun-30.png\r\n",
      "Rainbow-48.png        Summer-48.png\r\n",
      "Running-48.png        Thanksgiving-48.png\r\n"
     ]
    }
   ],
   "source": [
    "!ls Data/icons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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M7Nol/RFAFEWsX38Zfn6V0arVS4iNTUHHjqtx+PAtdO1aGxMmtIGfX2XMmXMaXbv+bnR8\nIiMiUTkSGLhSlMmCxTt3Hptc/r//HRQFIUgcP36nmJ2dbUg/ffqeaGc3VfT0/FlUqbJEURTFjIws\nURCCxDZtlkr24eMzS/T2/kVMSkqXpE+e/LcokwWLU6YcNaS1br1UlMmCxcxMjUXnod/u4cNUk8v/\n7/8OiIIQJLZrtyLfbfXH3Lv3pigIQaJMFizu3h1hWC8uLkWsUeNX0c5uqhgToyuvDz7YJcpkweLp\n0/ck++zSZbUokwWL0dFJBeZ78OA/RUEIEv385opPnmQY0teuDRcFIUjs3v13Q9rkyX+LghAkBgf/\nLdnHqVN3RRubyWL79jnnVtD5mnL9+iOj45myZctVURCCxOHDtxjSLLm+rVotEZXKKWJionRdX985\noo/PrAKPHRi4UnR0nCaq1Tn3Rt26c8UKFb4XZbJgMTQ05xq89dYm0d5+qpierhZFUVfOMlmweONG\ngmEdL68ZYpUqMyTH0F+PV15ZJmZkZBnS16+/JApCkDh48J8F5lHv9Ol7oiAEiQMGbDSknTwZIwpC\nkOQ66X3xxT5REILE+vXnSe6D/fsjRUEIErt0WW1ImzfvtCgIQaKHx8/ivXtPDOnXrz8S5fJgsU6d\nOYa0BQtCRUEIEvv33yB5pm7cSBDd3H4SHRymiQ8epBjS3dx+EqtVmynJ22uvLRcdHKYZvUesWHFB\nFIQg8b33dhjSdu68IQpCkPjNN4cNaampmaIgBIlt2y43pA0atEmUyYLFn346Idnnn39eEQUhSGzS\nZGGRztcUtVojVqs2U7S3nyoeP35Hsuyjj3aLghAkfvrpHsnxZLJgcdmy8wXuV+/bbw+LMlmwKAhB\noiAEiU5O08UuXVaLP/10wuTzb8k1uXz5oeG96PDhaMO6+udQJgsW16wJM6RfuRIvCkKQ2Llzzv2y\nfPl5o+skiqIYEZEgVqjwvejnN7fQcxwwYKMokwWLUVFJ4vTpx0SZLFg8dequZJ0ePf4QW7RYLIqi\n7nl2dp5uWHbwYJThcyQ3tVoj1qkzR5TJgsXY2KeSc3777Zz3mJYtl4hyebC4YcNlyfYzZvwjCkKQ\n2LfveqO8jhq1rdDzyn28unXnikFBR/L9t2XLVcM2+ufyq68OSva1aZPu/v3111OiKIri48cq0cXl\ne7FGjV/FuLic50yrzRZ79vxDlMmCJfekm9tPokwWLP7vf9L9Dh26WZTJgsU//gg3pH3xxT5RJgsW\nDx3KuS+6dVsjyuXB4qNHaYY0tVoj1qs3z+i905Rq1WaKL700U3z8WCVJ19/j339/3JA2YMBGURCC\nxI4dV0nu48WLz4qCECSOHfuXIa1fvw2iTBYsKcOUlEzDZ2+vXmsLzJf+eDJZsPjuu3/le42GDt0s\nCkKQ+NFHuw3bffnlfqM0UdR9biqVU8QqVWYY3utNPfvNmy8WZbJgUavVff+ZMyfE6D1DFEXxww93\niYIQVOT7/tIl3X04aNAmybqHD0eLghAkTp2q+xz9/vvjokwWLG7efFWy3n/+s02UyYLFY8duF1qW\n9GJjjSy9UFavDkfFinaYObOb5FfJli2rYuzYpnj0KL3A6Ri02mzMnNkNK1f2QaVK9pJl7dvXhCiK\niI83/hXzeRAEAW++Wc/s9Tt3roXu3esYXnt6OuHLL1+FWq3Fn3/qatXEfzsq6Zv86K1b1x8JCRPh\n41PJrHwFBbVHhQq2hrQhQxqheXNvHDgQbWgutHz5RXh6OuGbb9pJtm/d+iX07VsPx4/H4Nat5CKf\nrzlsbXUDAT19qqsZsPT6jhzZGBpNNjZtumJIO3XqLqKikiSjJZvSo4cvVCqN4Zf+2NgUREQkYuzY\nphBFEceO6ZpliqKIQ4duITCwptHo1uYQBAETJrQxnCsA9OqlG6U1Ojo5v80kVq8OgyAIGDKkoSGt\nTZtqqFmzIo4fj5HUbOQ+7rhxzSX3QefOtaBUyo2a2wLAsGGNULVqBcPrunXd4ONTSbLuypVhkMtl\nWLCgp6Q23s+vMiZOfAUZGRqTNeS5ffXVa1i7tp/RYHD6geMsfX5TU9XYuvUa6td3MxpMqX9/f7z+\nui/Cwh4aTbNhzvmacujQLdy79xTvvNPE0EpE74cfOsPNzQErVxrXgporOLgDjhwZib5968HeXoH0\n9CwcOnQL//3vQdSpMxcffbRb0lywKNekYUMPdOiQM8Ddq6/qzsPNzUHy3Pj7u8PRUSkpk2XLLsDG\nRoaZM7tJ9lmnTmWMHh2AyMgkoybNBenf39+oefGTJxk4dCgagwY1MLmNr68rVqzog//9r60kXaGQ\nG2oWHz0y3Szy+vUEnDlzH9271zHa/+eft4G/vzt27Lhh1Kwy7+Buhbl5MwmTJx/L99/WrTldCzp1\nqoUqVZyxceMVyT42bLgCuVyGwYN1z/2mTVeRkqLG//7XFp6eTob1ZDIB06Z1hCiKRrXnAIyeix49\nfCGKYqH3uijq/uVu5qpQyHHkyEjExX1h1I87N40mG7Nnv46VK/vAxUU6a0F+n9WCIOCTT1pJ7mN9\niwZ9XtPS1Ni1KwKtWlXFm2/mXBMnJ6WhDMwliiKWLr2Q7zVav/6yUQ3v6tXhcHW1xy+/dJWkt279\nEt55pwkePkwzqgUtyLp1l2Fra4MpUzpK0n/4obPR540l933Dhh5o0qQKdu6MkHR3+P13XWulYcNe\nNpSBKIo4c+a+pOxmzeqGuLgJaNtWOqAnUV4ctZheGAkJ6bh//yk6d64l+VKv99pr1TFvXqihGZIp\ncrkMffvqgqk7dx7j0qV4REcn48qVeBw5ctvQPKmk+PhUNHvddu2qG6W1bFkVoigiLEzX9HHUqCZY\ntuwCPvlkL3755RS6d/dF9+510LVrbdjZmf92kXc0af2xzp17gEuX4vHyy564c+cxqlVzweTJR43W\n1fcTu3gxThI8W3K+5tA3bXNy0k1xZOn1HTy4IT77bB/Wrr2M997TTUXzxx+XIAiCGYFsHfz3vwdx\n6NAttG9fE4cORUMQBLz3XjMsX34Bx47FYMKEVxAaeh+JiemGZsVF4esrHXnbwUEBW1sbpKaq89ki\nh0aTjQ0brsDJSYmePetIlg0d2gjTpx/H0qXn8cMPnY22zTvityAIcHJSIjNTa5RuanRwFxdbaLW6\nLzqCICAsLA516riaHHzntdeqS+7l/Oi/mD54kILw8IeIikrG1auPcPy4rv+zVmvZqDOXL8dDo8k2\nec/r87VvXxTCwuLQtGkVi87XlIsX4yAIgskveA4OCgQEeOHQoVu4dSvZrB+eTNGPCJ+ZqcGJEzE4\nfPgWduyIwJUrjzB//hlkZGiwZElvACjSNcl7Pzo66r4w16xp/Hzb2dlI+mGfP/8Ajo5K/PzzP0br\nRkc/hiiKuHgxzuwvwH5+ldGggQe2br2On37SDWa1ffsNZGVl59usuEaNihg5MgBZWVqcOxeLiIhE\nREYm4cKFOENz/fw+By5e1DXfbdvW+P1YEAS88ko1XLuWgPDwh5Jg39L3vp496+Cvv4aYta5MJmDw\n4AaYNes0zp6NRfPm3khLU2P37pvo2NEHXl66oFX/Y8zJk3eN5iLWN0G/eFF6rd3cHIymv9MHlqb6\n1+f27rtNcfBgNN54Yy3q1XND9+6+6NGjDgIDa5ocZTo3GxuZIdC8ffsxLl+OR1RUEq5ceVTgZ3Xe\n5zInr7r3rGvXEqBWayXN6/XatHmpwDzlJQgCzp4diyZNqphcvmtXBHr1ypmTNi4uFQ8fpuL1131N\nBvGvvVYdCxeeRVhYnNk/fISHP0SdOq6Gz0A9R0clGjXykPQ7t/S+HzHiZXz++X5s2XINb7/dGGq1\nFlu2XMdrr1U3POuDBzfEjz/+gx9//AcrV4bh9dd9//3e4Qt3dw6yRoVjIEsvDH2tW95fZ/X0/d8K\nG9TowoUH+OSTvThxQje/oEIhQ8OGHmjRoioiI5NKdPRFS2roctf+6Om/oDx5oiub5s29cerUO/j+\n+xPYsycSixefx6JF5+DsbIsJE9qYPZ2Dqb6EOcfKMAwKce/eU0yefMzkPgQBSEpSSdKKUiNZEP2v\n7LVq5XxJtOT6Vqpkj549/bB9+3Xcv/8UXl5O2LjxCpo394afX+UCj92ggQeqVXPBoUO3MHlyBxw+\nfBve3s6oU6cy2ratYRhYau9eXf9YfQBWFPn9CGFO7cGuXRFITEyHIAiwt59mcp3Vq8MwbVpHoy+X\npn4wEgTB5HHzW1eXTyArSwu1Wlvs5zc6OhkffbQHe/bchCAIkMsF1KvnhpYtq+LSpYcWT7OS875i\na3K5t7czRFE0yldh55tfVztzjgcUXg7msLW1QadOtdCpUy1Mm9YJf/55FcOHb8HKlWGYPr0TXFzs\ninRN9IGr8fEK7vOuVmuRkaFBZqa2gPcNweh9ozD9+9fHlCnHcOnSQzRq5InNm6+hSRMv1KiRf/A4\nZ85pfP/9CTx8mApBEODqao82bV6Cn19lXLgQl+/nQFGv37N+78tr+PCX8euvIdiw4TKaN/fGtm3X\noVJlYdiwnLloHz/OgCiKWLMm3OQ+BEFAcrK07E1dU/29Xdij1r+/P/btG46ZM0/h8OFbmDXrNH79\nNQTu7o6YNq1joVPJnDsXi08+2YuTJ+9CEAQolfJ/38u9ERVl+rM673OZk1fdyvrzc3aWBn6A7hpZ\n8oOvbr/mr/usvsPoZWVpkZ6eBWdn0/eiq6u9UZol9/3QoY0wceIBrFt3GW+/3Rh//XUDT55k4O23\nc37k9fGphLNn38W0acexfft1rF4dhlWrLsLOzgZjxzbFzJndCv3Rgl5sDGTphaH/4Ll//6nJ5foP\nqIKmqXj8OANduqxBZqYWc+Z0R4cONVGvnhvkchmOHbuDdesKbtZYmlQq4w83fUDp5pbzgdWkSRVs\n3DgQWVla/PPPXezefRMrV15EcPBR1Kjhku8ARrllZGiMvnjlHMvB8Otvly61sHfv8CKfU3EdOxZj\nqAXR59HS6ztyZGNs3XoNmzZdRYMG7khISEdQUKBZx+/e3RfLl19ASkomjh69jfbtdbVIgYE1sHNn\nBMLDH2L//mjUr+9W4Jfq52nVqjDDID+VKxt/sdm3Lwq3bz/Gjh0Rhtrs50GplEOplJvx/BrnUU+r\nzUbXrmtw9+5TTJnSAW+84Yf69d2hVMoRHZ2M5cuNm0UWJud9JcXk8uRkFQRBMGv6m2dzPN1zZunx\nRFFEw4a/oVIlO5w4MdrkOgMG+GPbtutYt+4yoqKS0br1S8W+JpZQKuVQKOSoU8cVly+Pfyb7BHSB\n7OTJR7FlyzX4+FTC/v1RCA4OzHf9FSsu4NNP96JVq5ewfHlvBAR4oUoVXRAxYsRWXLgQl++25twv\ngOXXr7iaNKmC+vXdsWnTVfz8c1ds2HDFMBCUnpOTEoIgIDR0DJo1M66RfB46d66Fzp1rIT09C3//\nfRs7d0ZgzZpwvPfeTtSr52bUvF4vKUmFLl3WQKsVMW9eDwQG1kTdupUhl8tw+PAtrF9/2eR2hdF3\nOdH/+JubRpNt1NrkWXoW32FyUyjkcHRU4smTDJPL87bYsfS+d3d3xOuv+2Lv3kg8fpyBjRuvwM7O\nxqjFm77JcnZ2b4SG3seePTexYsVFzJt3Bh4ejvj6a2n3I6Lc+DMHvTDc3R1RpYozrlx5ZPKN++jR\nOxAEAQ0auAOAyaZ9+/dHISlJhQkT2uDDD1uiQQMPw6+F+iY4ltbolJQzZ2KN0vT9M1u31jWJWrHi\ngmE0SIVCjsDAmvjppy5Yu7Y/RFE0NL8s6rFsbeUICPCCh4cjPD2dEB7+UDIqqt7y5RcwefLRfD+w\nn4XIyCTs3x8FDw9HdOlSG0DRrm+PHnXg5uaAHTsisHNnBBQKOd56y3TT9Lx69KgDrVbEunWXcfv2\nY0Mg26GDD0RRxLZt1xEaet+oSa8pz2OwzORkFXbvvgk3NwesW9cfCxb0NPr35ZevlNicso0be+HB\ng1STfeuOHtX1i9Q/v4BxmZw+fR/R0ckYPrwRvv66HRo39jL0h8u5vsi1feGF2rCh7h4JCbln8tk3\nla/iCAjwgiiKOHHC+FnUaLIREnIPlSvbw9PTsmZ5+nM9ffo+oqKM+zznXU9f+2PpNSmuRo08EBWV\nbPI9fMvFouLvAAAgAElEQVSWa/j22yMmp/oqeJ+e8PV1xdat17FrVwTUam2+0+4Aun6FgiBg06aB\n6N69juHLPKBregrk/zkQEOAFACavH6ArM7lcQN26BbfoeB6GD2+Eu3ef4tixO9i/Pwq9evlJmpy+\n/LLnv/0Zjd/f4+JS8fnn+4z62ZrL1LM2Y8ZJTJumq3l3cFCgR486WLCgJ37+WdcEPL8yBGAIniZO\nfAXjx7eAv7/7M/ms9vd3h52djWSke71z52Kf6+d/lSrO8PBwxKVL8YZuMbnl/Q5jjqZNqyAiItGo\nT7ZarcWlS/GStKLc9yNGNIZWK2L79uv/3lN1JeMmbN58FR98sAtqtRYymYDWrV9CcHAH7N//tkXf\nOejFxUCWXij/+U9jpKRk4osv9kumNzl9+h4WLjwLd3cHw4BINja6xyP3XKX6ZkNxcdL54qKjkzFt\n2nEIgoCsrJLrI2uJ9esvSwbNuH//Kb7//gQqVrQz9CU6cSIGs2aFYOfOCMm2+kGXTPVhy0sURXzz\nzRFJ86Y//ghHSMg9DBzYAI6Oui9GI0c2RlxcKiZNOizZPiwsDh99tAdz5px+brUS8fFpGDJkM7Kz\nRXz3XXvDtS7K9bWx0Q2Gcvz4HWzZch2vv+5rdr47d64FhUKG778/AUEQDNNqNG7siUqV7PHrryHI\nzhYl0+nkR6GQF2teXVPWrbsMtVqLt95qAJnMdFA3eHBD2NnZYN++KMTGmq5lelb+85/G0Gqz8fHH\neyT9627cSMAPP5yAg4NCEoDkLRP99X34UDrIS3x8Gr766tC/11eba3vj94C8nJ1t0a9ffURFJWP6\n9OOSZVu2XMOOHRFo3NgTjRp5FuGMjXXuXAtVq1bA77+HG6YK0vvyywN49CgNw4e/XKRpQD78sAW0\n2mwMHLgpn8D0NjZvvorAwJqGwbIsvSbF9Z//BCAzU4OPP94r6Y93585jvP/+Lvz00z/5Nr0sSP/+\n9REe/hCzZp1GQIAXatXKv39xfu8TCxacwblzuiAvv8+B+vXd0aJFVRw9ege//y5tojtrVgjCwx/i\njTf8jAabKwn6AXg++WQvsrKyjfr5DxrUAPb2CkyZcgx37kjvj88+24dZs0Jw717Rfnw09azt2BFh\naPKdm/7zqEYN6YBtueV3jSIjkwzvt0X5rHZwUGDgwAYID3+IJUvOGdIzMjSYNOnIc59+Z+TIxoap\nunIHjSdP3sWSJefh6emIbt18zd7ff/6jG7BwwgTpd6LJk48a/VhUlPu+d++6cHGxRVDQUaSkqDF8\neCPJ8vDwh/jtt7NGrWEs+c5BLzY2LaYXyqRJ7XDgQDSWL7+Is2cfoEOHmoiNTcG2bdchl8uwevWb\nhjdrmUyAl5eu1vCjj3ajZ08/BAbWRNWqFbBkyXnExqagYUMP3LnzBH/9dQNOTkqIoojERMv6Z5UU\nOzsbBAau/DeYVGDLlmtITFRh1aq+hr4wX33VFtu23UC/fhvQr1991KpVCdHRydiy5RqqV3fBuHHN\nzTrWpUsPERCwED171sGtW7pmpz4+lfDjjzkDAn37bXscPBiNn38+icOHb6Ft2+pISsrApk1XkJWl\nxdq1/Szub5RXZGQSgoP/NrxOT8/CrVuPsXdvpGFew9znVNTrO3JkY8ybF4rY2BT8+ms3o+X5cXBQ\noF27Gjh4MNrQPxbAv4P5VMdff91AxYp2hlFdC1K1qjNOn76PUaO2o2vXWhgypFGh2xRGP1px7n5y\nebm46H4IWbfuEpYvv4BJkyxvBmZuLcZ77zXHzp03sWvXTTRuvBDdutVGcnIGtm27jowMDZYt6y2p\nJaha1Rnnzj3A6NHb0a1bbfTv749GjTyxZ49uTuCWLb3x8GEatm+/gexsEUqlXHJ99f3KV6/WjQI8\ndmxTQ1/v3GbPfh1nztzHt9/+jf37o9GihTciIhKxa9dNVK5sj9Wr3yzS+ZqiUMixalVf9O69Dl26\nrEGfPvVQvXoFHD8eg7NnY9G0aRVMmdKhSMd7//0WCAt7iCVLzqN+/fno0qUW6td3gyjqBvo5cuQ2\natRwwcqVfQzbWHpNimv8+BbYuTMCv/8ejnPnYtG5cy1kZGiwceMVPHmSiXnzupu8RoXp398fP/74\nD0JD72PatI4Frjt8+MvYuTMC3br9jsGDdT/OnTx5F6dO3YOnpxPi49OMarhyW768NwIDV2HEiK1Y\nu/YS/P3dcf78A/z99234+FTCvHk9LM5/XhERiZL3PlPatashGVCqenUXvPZadRw/fgeurvZ4/XVp\nQOTh4YgFC3pgzJgdePnlhejbtx48PR1x6NAtXLwYZ5jTuCiqVq0AURQxadJhHDt2B9Ond8LUqR3Q\npcsavPLKcgwc6A9PT11t5J49kWjatEqBP5B07OiDKlWcsXDhWdy9+xQNGrjj9u3H+OuvG3B2ti3W\nZ/XPP3fB8eN3MG7cLvz1VwR8fSth374oqNXa59IyJrfvvgvEoUO3sHjxeYSGxqJ9+xq4f1/3HUah\nkGHNmjcloy4X9uyPGtUEmzdfw5o14bh8OR7t29fAhQtxOHXqHl56qYKhSxBQtPteqdS1UFq06Bzc\n3ByMxnr48MOWWLkyDB9+uBt79kTC398NDx6kYtOmq3B1tccXX7zyDEqNyjPWyFK5U9AHib29AseO\njcK337aDSpWF3347i2PH7mDAAH+Eho5B1661Jev/9ltPVKvmgqVLL2DXrghUqGCLQ4dGoHfvuggN\nvY/5888gPPwhRo8OQHj4ONSr54a//74t+VW5qB9sxflANLXtu+82wzfftMORI7fw++/h8Pd3x/79\nwzF0aE6Q4uvrin/+GY0BA/xx+vR9/PprCE6evIt33mmCU6fegZtb4TWN+qZHjRp5YtmyCwgNvY93\n322KU6fekXzBdHBQ4PjxUfjmm3ZITVXjt9/OYt++SHTs6INjx0ahT5/i9bcUBAFRUcmS6Qxmzz6N\nkJB76Nq1NnbuHIL583tKtinK9QWAZs284eNTCS4utujdu65F+ezRo46kNlavQ4eaEAQBr7/ua7I2\nNO81njGjK+rVc8O6dZewYcOVfNeT7iP/hZGRSQgNvQ9fX1e0alXwaJyjRgVAEASsXHmxwPXyO25B\n+ci9SCYTsGPHEMyY0QW2tjZYsuQ89u2LQqdOtXDs2Ci8/XZjyba//NIVdetWxrp1l7Fx41XY2Miw\nZ88wDB3aCDduJGDu3FCcOnUPb75ZDxcvvoe2bavj4sU4PHyoq3Hw93fH11+3hVqtxfz5Zwx9wARB\nkOTZy8sJZ86Mxccft0RMzBPDffPee81w/vx7aNjQo0jnm5+OHX0QEjIGb75ZH0eP3sZvv51FWloW\npkzpgH/+GW1o9WDO8fJauPAN7N07DG++WQ+XL8dj/vwzWLToHBITVfjuu/YID38f1arl1IRZek3y\nll1h6XnzL5MJ2LVrKH7+uQtsbGRYsuQ8tm69jmbNvLFnzzC8/755gVTeQzVv7o3q1V0gCDAZIOXO\nw6BBDbBqVV9Ur+6C1avDDVOKLFvWG5s3DwIA7N6d/xQoDRp44Ny5dzFqVADCwh5i/vwzuHv3KSZO\nfAXnzr1rNFiepZ8FgiAUOv3O5MnHcOTIbaNthw9vBEEQMGhQA0NLldxGjgzA4cMj0LZtdezcGYEF\nC85ArdZi6tQO2L17qInBkvK/prkXjRzZGG++WR/Xrydg0aJzuH//Kdq2rYEjR0YiMLAmDhyIxqxZ\np3HjRiK+/PIVHD48osDpdypWtMOhQyPwxht+CAm5h/nzz+DSpXiMHdsUly+/D19fVxw5ckvSAiO/\ncs57b3p4OCIkZAzeeacJzp2LxdKlF1C7tisOHhwBmSz/+7go8paT/nNz0qS2SEvTfW6eOBGDt95q\ngNDQsejUqZbR9sb7lL7evn0wJk8OxJMnmfjtt7NISVFj584hqFvX7Znc9/o+sYMHNzQauMnd3REn\nTozC6NFNcOVKPGbPPo39+6PQv399hISMMRrhnCgvQSyrHfqI6JnYty8S3bv/gf/7v9cwfXqn53qs\nIUM2Y+PGK7h27YNCR+0tTxIT0+HtPROjRgVg4cI3Sjs7RFRGXLr0EI0bL8TIkQFYsaJP4RsQlTMz\nZ57CxIkHcPr0GJPTFhEVB2tkiYiKafr049BosjF2bMHTQRDRi+XBA13tfqVKlvfbJbJ2iYnpmD//\nDF5+2ZNBLD0X7CNLRFRErVotRUpKJq5fT8Abb/iV2JQURFS2xcamYOLEAwgNvQ9BEPDqq9VKO0tE\nJWbPnpuYNOkI7tx5jOTkDGzd+lZpZ4nKKQayRC+AvP1snu+xSuY4ZYGrqz2uXInHG2/4YeXKvqWd\nHSIqI65de4S9eyORnS3ik09aoX//ZzdyM1FZV7VqBdy//xQ2NjLMmNHF4rEjiMxl1X1ks7OzodFo\nCl/xBWZjY8MysgDLyzwsJ8uwvCzD8jLGMrEMy6twLCPLsLzMw3KyDMvLPEql0mS6VfeRlcmsOvsl\ngmVkGZaXeVhOlmF5WYblZYxlYhmWV+FYRpZheZmH5WQZllfxsPSIiIiIiIjIqjCQJSIiIiIiIqvC\nQJaIiIiIiIisCgNZIiIiIiIisioMZImIiIiIiMiqMJAlIiIiIiIiq8JAloiIiIiIiKwKA1kiIiIi\nIiKyKgxkiYiIiIiIyKowkCUiIiIiIiKrwkCWiIiIiIiIrAoDWSIiIiIiIrIqDGSJiIiIiIjIqjCQ\nJSIiIiIiIqvCQJaIiIiIiIisCgNZIiIiIiIisioMZImIiIiIiMiqMJAlIiIiIiIiq8JAloiIiIiI\niKwKA1kiIiIiIiKyKhYFsmq1Gr169cKpU6cMafHx8Rg/fjyaNGmCjh07Yu3atZJtQkJC0Lt3bwQE\nBGDEiBGIiYkxLHv77bcxe/ZsyfoJCQno0qULBg8eDJVKVZRzIiIiIiIionLM7EBWrVbj888/R2Rk\npCFNFEWMGzcOarUaW7ZswYQJE/D9998bAt0HDx5g/Pjx6Nu3LzZv3gw3NzeMHz8+32OkpqZizJgx\ncHZ2xtKlS2Fvb1+MUyMiIiIiIqLyyMaclaKiojBhwgSj9GPHjiEmJgarVq2Cs7MzfHx8cObMGVy4\ncAFt2rTBxo0bUb9+fYwePRoAMH36dLz66qs4deoU2rRpI9mXWq3G+++/D7VajT/++ANOTk7P4PSI\niIiIiIiovDGrRjY0NBRt2rTBhg0bIIqiIT0kJAStWrWCs7OzIS0oKMhQ6xoeHo7mzZsbltnZ2cHf\n3x8XL16U7D87OxufffYZYmNjsWLFClSqVKlYJ0VERERERETll1k1skOGDDGZfvfuXVSpUgWzZs3C\ntm3b4OzsjJEjR2LAgAEAdP1nPTw8JNu4ubkhLi5OkvbNN9/g8OHDWLRoETw9PYtyHkRERERERPSC\nKNaoxWlpadi+fTsSEhKwYMECjBgxApMnT8ahQ4cAABkZGVAqlZJtlEol1Gq14fWmTZtw6NAh+Pn5\nYcGCBcXJDhEREREREb0AzKqRzY9cLkeFChUwZcoUCIIAf39/XL9+HevWrUOnTp1ga2srCVoBXV/Y\n3E2HVSoVVq1aBYVCgf79+2P58uWGPrXmyBsokzGWkWVYXuZhOVmG5WUZlpcxlollWF6FYxlZhuVl\nHpaTZVheRVesQNbDwwOiKEIQBEOaj48PQkJCAACenp5ISEiQbJOQkAA/Pz/D62HDhqFhw4YAgHfe\neQdz5sxBhw4d4OPjY1Ye8gbKJJW3BpwKxvIyD8vJMiwvy7C8jLFMLMPyKhzLyDIsL/OwnCzD8jJP\nfsF+sZoWN2nSBDdu3IBWqzWk3bx5E1WrVgUANG7cGOfOnTMsU6lUuHr1KgICAgxpcrnc8PcHH3wA\nLy8vfPXVV5JBpYiIiIiIiIj0ihXI9ujRAwqFApMmTcLt27exfft2bNu2DcOGDQMA9O/fH+Hh4Vi0\naBGioqLw9ddfw9vb22jqHT2lUokpU6YgLCwMy5YtK07WiIiIiIiIqJyyOJDN3YzY0dERK1asQFxc\nHPr06YO5c+fiu+++Q/v27QEAVatWxdy5c7Ft2zYMGDAAycnJkgGdcu9Lr0WLFhg4cCDmzp2L6Ojo\nopwTERERERERlWOCaOVteNmuvGBse28Zlpd5WE6WYXlZhuVljGViGZZX4VhGlmF5mYflZBmWl3me\nSx9ZIiIiIiIiopLGQJaIiIiIiIisCgNZIiIiIiIisioMZImIiIiIiMiqMJAlIiIiIiIiq8JAloiI\niIiIiKwKA1kiIiIiIiKyKgxkiYiIiIjIpKwsLdLTs0o7G0RGGMgSEREREZFJQ4ZsxauvriztbBAZ\nsSntDBARERERUdkUHf0Yt249fqb7VKvViLgUjuSw83C6EwlFZibc4mNhl5kJx+RkAIBi9/Fnekwq\nfxjIEhERERGRSVlZWmRlZT+TfcU9iMXN/TvRYscW1E1Peyb7pBcXA1kiIiIiIjJJrc6GRpMNURQh\nCEKR93N82Xy03rENldUZOYlyGW63qwxNJRF29nGwy05F9bRoAAB75VJhGMgSEREREZFJGo323/+z\noVDILd7+7PGjqLl4NlonPtIlyAWktUmDwvkx5C4paKB9oktX/7uB7TPINL0QGMgSEREREZFJarUu\nkM3KsjyQPbH+D7RavVD3wlmB1DoZUNaLhkeWrh8stECKrTNUDo5QIAs20MBZm/Iss0/lGANZIiIi\nIiIySd8/NitLC0Bh1jZarRbnf/gOrf45CggAlCIyekTCM/shkAVkyWyQWMkTid5VUS/rEpyF5Fxb\nm3cMIgayRERERERkkj6QVavNH/Dp/A/fIeCUbtThxw1sUcn/PCpn6wZ3ulH9ZXjZxcJdlggvTYIu\n0CUqAgayRERERERkUk7TYq1Z659Y/7uuJhbA0xbZcPc5DaWoxmP7isjwdkZd5Q3d/qDABaEhbqan\nIlLmjzsaW2RmZ+Nxli7g3fwczoXKFwayRERERERkRBRFQwBrzhQ8Z479jVarFwGCriZWH8SmOjhB\nrKaElyweWlFAjOiG1epXcTY9898t1cgZ7YnIPAxkiYiIiIjIiFYrQhR1f5tTI/vSghm6P2xFuDS4\nCGW2GqkOzrCpIcAJutGJ52AYjj5JglbMNGxXUdEPSektoVLb4W6K8pmfB5VPDGSJiIiIiMhI7uC1\nsBrZ48sWoHXCI8BZgYwekaicnYLH9hUhVlPCCU+QDHcsyGiBkPREAIBS8EXc0zfxRNUA6VrLp/Uh\nYiBLRERERERGcgevGk3+geyD2PtovWMLACClbga8sh8CADKqOsNLFg8A/waxIiAKyNZ2QsTj4UjL\n0gWwztlqOGWkIysjC+mZ/1YB46XncEZUnjCQJSIiIiIiI/qBnoCCmxZH7t8JN3UmIBdg6xcNZOlG\nJ66ruAGtKGAOhiMkPQEQBVSQv49TcW0AALaiBq1SYnDvzH1oskUoADgY9spAlgrGQJaIiIiIiIzk\nrpHNr2mxWq1Gi51bAQDpr6TBPSsZWTIbeNk9AADcEd1w9ImuOXG2tpMhiK2WngxlWATupGZCNLln\nooIxkCUiIiIiIiPm9JG9cSkM9dJ1U+YonR4DABIrecJdlogsKLBG/Sq0YiaUgi8iHg8HoAtiHcJu\nQJWqhq1cQOMMGXzVAmwT1dCkmjfNDxEDWSIiIiIiMmJO0+LHFy/o/pDLIFRMATRAkrc3vDQJOI9G\nhil24p72Q1qWHLbZGijDIqBKVcMBwKBEGbSxGQAAzXM9GypvGMgSEREREZGR3LWwuYPa3Jzu3AQA\n3G5XGQ00T5Bi64y6WZcBAYhUpRnWe6LyBwC0TLmLmNRM2MoFDEqSQROrggABFes4wftjB9TwrwG5\nnKMYU+EYyBIRERERkRFzmhYrMnU1rpqKunVVjo5wFpIBANdk9QFkoqJNP6Rr5XDOVuPe2XsQATTO\nkEF7PwMCBLi+J0fnMS1ha2v7XM+HyhdZaWeAiIiIiIjKHnOm33F/qBvUyd5BN+WOAlmGZfc1SgBA\nYnorAICTKg3abN3QTr661sSoWMcJnce0ZxBLFmMgS0RERERERqQ1sqabFldITdX9r30EALAVMw3L\nUjS6oFaVpQtSM1U5Qa5tku5v708cGMRSkTCQJSIiIiIiI2p14dPv2KTrAllPla5m1kGbblim0uqq\nXe+m2AEAMrJyJtrRpOkCY58GPs8wx/QiYSBLRERERERGzOkjW1wc2ImKioM9ERERERGRkdzBa35N\nixW7j+uWA1AqlVCr1YZlm43WrvbvP6LiY40sEREREREZkc4j+3xqZImKioEsEREREREZyT1ScX41\nskSlhYEsEREREREZYY0slWUMZImIiIiIyIg50+8QlRYGskREREREZEQ62BNrZKlsYSBLRERERERG\n2LSYyjIGskREREREZMSc6XeISgsDWSIiIiIiMsIaWSrLGMgSEREREZERTr9DZRkDWSIiIiIiMiId\ntZg1slS2MJAlIiIiIiIjanVO8Jq7dpaoLGAgS0RERERERjiPLJVlFgWyarUavXr1wqlTp4yWZWVl\noVevXpg3b54kPSQkBL1790ZAQABGjBiBmJgYw7K3334bs2fPlqyfkJCALl26YPDgwVCpVJZkj4iI\niIiInhHOI0tlmdmBrFqtxueff47IyEiTyxcsWGC0LC4uDuPHj0ffvn2xefNmuLm5Yfz48fkeIzU1\nFWPGjIGzszOWLl0Ke3t7c7NHRERERETPEEctprLMrEA2KioKgwYNwr1790wuv379Ov7880/UqlVL\nkr5x40bUr18fo0ePRu3atTF9+nQ8ePDAZI2uWq3G+++/D7VajWXLlsHJyakIp0NERERERM9C7uA1\nd1BLVBaYFciGhoaiTZs22LBhA0RRlCzLzs7G119/jYkTJ8LFxUWyLCwsDM2bNze8trOzg7+/Py5e\nvGi0j88++wyxsbFYsWIFKlWqVNTzISIiIiKiZ4B9ZKksszFnpSFDhuS7bOnSpXB1dUXv3r2xfv16\nybL4+Hh4eHhI0tzc3BAXFydJ++abb3D48GEsWrQInp6e5uadiIiIiIieE06/Q2WZWYFsfm7duoUV\nK1Zgy5YtJpdnZGRAqVRK0pRKJdRqteH1pk2boNFo4OfnhwULFqBdu3bFyRIRERERET0DuYNXTr9D\nZU2xAtmvv/4a48aNQ5UqVUwut7W1lQStgK4vbO6mwyqVCqtWrYJCoUD//v2xfPlyjB492uw85A2U\nyRjLyDIsL/OwnCzD8rIMy8sYy8QyLK/ClaUy+vbbI4iLS8Xixb1KOyv5KkvlVVI0GlHytzll8CKW\nU3GwvIquyIFsbGwszp8/j2vXrmHWrFkAgMzMTISHhyM8PByLFy+Gp6cnEhISJNslJCTAz8/P8HrY\nsGFo2LAhAOCdd97BnDlz0KFDB/j4+JiVj7yBMknlrQGngrG8zMNysgzLyzIsL2MsE8uwvApX1spo\nw4bLePAgFfPmdSvtrJhU1sqrpGRmagx/q9WaQsvgRS2nomJ5mSe/YL/IgayXlxcOHDggSfv000/R\ntGlTjB07FgDQuHFjnD171rBcpVLh6tWrkil45HK54e8PPvgA+/btw1dffYV169ZBEISiZo+IiIiI\nrERmphYZGZrCV6QSpe8ja29vwz6yVOaYPY+s0YYyGapVqyb5Z2trCxcXF8MAT/3790d4eDgWLVqE\nqKgofP311/D29kabNm1M7lOpVGLKlCkICwvDsmXLipo1IiIiIrIiKlUWsrKyodUyWCpL9MGrg4OC\noxZTmWNxIFtQLWneZVWrVsXcuXOxbds2DBgwAMnJyViwYEGB+2rRogUGDhyIuXPnIjo62tLsERER\nEZGVUal0tbGslS1bsrK0kMsFKJVy1shSmSOIeSeGtTJsV14wtr23DMvLPCwny7C8LMPyMsYysQzL\nq3BlrYwcHX+ERpON2NhPULmyQ2lnx0hZK6+S8sorK3D1agLc3R0gCAIiIsYXuP6LWk5FxfIyT359\nZIvctJiIiIiIqLg0mmzD1C4ZGWy+WpZkZWVDoZBDoZBDrea1obKFgSwRERERlZrczYnZtLhsycrK\nhlIpg0IhYx9ZKnMYyBIRERFRqdH3jwWk071Q6VOrtYYaWfaRpbKGgSwRERERlRqVKsvwN2tkyxaN\nJhsKhb5GloEslS0MZImIiIio1OQOXnPXzlLpy6mRZdNiKnsYyBIRERFRqWEf2bIrK0v7b42srmmx\nlU92QuUMA1kiIiIiKjW5a2EZyJYt+lGLbWx0IYNWy0CWyg4GskRERERUaqSDPbH5almiVmsNoxYD\nYPNiKlMYyBIRERFRqWHT4rIr9zyy+tdEZQUDWSIiIiIqNRzsqWwSRREaTTaUSjlrZKlMYiBLRERE\nRKWG0++UTfraVxsbGZRKXY2sWs0aWSo7GMgSERERUanhYE9lk772VT+PbO40orKAgSwRERERlRrp\nYE8MZMsKtVoXtOqaFrOPLJU9DGSJiIiIqNRIB3tijV9ZoQ9ac0+/w0CWyhIGskRERERUajhqcdmk\nD1pzT7+j0fCHBio7GMgSERERUanJ3bQ498BPVLr0TYs5/Q6VVQxkiYiIiKyMKIrYteumIdiwZrmD\n18xM6z+f8oKDPVFZx0CWiIiIyMrs3RuFfv3+xPr1V0o7K8WWu18smxaXHRpNTh9Z1shSWcRAloiI\niMjKxMQ8BQDEx6eXck6KLyOD88iWRTlNi3PXyFp3IHv37lM0arQIJ0/eK+2s0DPAQJaIiIjIyiQm\n6gLY8tCnlPPIlk25Ry0uL02Lz5yJRUREEg4ejC7R48bHp+Hhw7QSPeaLwKa0M0BERERElklMVAEA\n0tMZyNLzkXseWRub8tG0+OnTTADAo0cl25Khf/8/kZ6ehXPnxpToccs7BrJEREREVqY8BbL64FUQ\nGMiWJTk1sjIoleWjRlYfyCYkqEr0uNHRyUhIUEGlyoK9vaJEj12esWkxERERkZXRB7K5azOtVUaG\nBu/XvgoAACAASURBVIIAODkpJQM/UenSB61KZfkZ7Ck1VQ0ASEgo2RrZp091x71163GJHre8YyBL\nREREZGWSkvSBrPXXyKpUGtjbK2Bvb8Ma2TJE37TYxqb8TL+jDyjj40uuv2pmpsZQlpGRySV23BcB\nA1kiIiIiK6NvGpmebv2Bn665pQ3s7BjIliU50+/Iyk2NbE7T4pKrkX3yJNPwNwPZZ4uBLBEREZGV\n0dfIlo8+slpDIJuZyUC2rMg92FN5mX4nJUUXVCYmqqDVlsy56INnAIiKyglk9+2Lwpkz90skD+UV\nA1kiIiIiK6JWaw1fjstD0+KMjCzY2ekC2fLQ57e8KI/T76Sk6JoWiyKQlJRRoscEcgJZlSoLAwZs\nxocf7imRPJRXDGSJiIiIrIi+NhYoL02LNYZAlk2Lyw59IKtUlp+mxbmDypJqXiytkU0CAFy8+BBq\ntdYwHzQVDQNZIiIiIiuiH7EYKB9Ni3WDPembFmshimJpZ4mQ07RYoZDDxqZ8NC3OHVSW1Fyy+gGm\nAODu3afIyNAgNDQWgLT/LFmOgSwRERGRFckdyFp7DWZ2tgi1Wgt7ewVsbXW1fpmZ1t18tbzQNyPW\nDfakCxk0Guu+NqVRI6vvl6tQyCCKuil4zpzRB7IZ/OGmGBjIEhEREVkRadNi666R1Qfi+qbFudOo\ndEn7yJaXpsU5NaAlNQWPvha4YUMPALqRi8+ceQBAV5784aboGMgSERERWZHcNUnWHsjqB6uys7OB\nvb3Nv2kMZMuC3DWySqUukNU3N7ZWuZv5lnQf2SZNPAEAp0/fx+3bjw3L2by46BjIEhEREVmR3DWy\nKpUG2dnW2zRRH7Tq+8gCrJEtK0yPWmy9NbKZmRqo1Vp4ejoCKPk+sk2aeAEANmy4kmc5A9miYiBL\nREREZEUSEnSBbOXK9gCsO/DLHcja2jKQLUtMzyNrvTWy+v6xtWpVBJDzHD1v+kA1IEBXIxsT8xQA\nUK9eZclyshwDWSIiIiIroq+RfemlCgCsu3lxZqZxH1l9GpWunBrZ8jH9jj5grFlTH8iW7GBPXl5O\nqFLFyZDesWNNAGxaXBwMZImIiIisiP4LeLVqukDWmvuU5tTIKti0uIzR174qleVj+p3UVF2NrKur\nPSpWtCvxpsUVKtjC17cSAKB27UqGgDr3AFRkGQayRERERFYkKUkFGxsZPDx0ff2suUZWH8ja2clh\nZ6er9cvIsN7mq+VJzjyy5WP6nZyAUgk3N/sSr5F1dlaidm1dINuiRRW4uNgCYI1scTCQJSIiIrIi\niYkqVK5sD0dHBYCckX+tkT7vuhpZ6z+f8qS8Tb+jb1rs5GQLd3cHJCSkl8hAaU+fquHkpIRcLoOv\nrysAoEULb1SoYCvJF1nOprQzQERERETmS0xUoUoVJzg46AK/9HTrbYqrr321s7OBXC4AAOfVLCM0\nmtx9ZK1/sCd902JdjawDtFoRyckqVK7s8FyP++RJBipUUAIAhg1riFu3HmPo0IY4fz4OAAPZ4mCN\nLBEREZGV0Giy8fhxBipXtjfMu1qcpsWlHZhkZOhrZDn9TlmT07S4fNXIVqhgC3d3/RQ8z3/k4pQU\nNZyddbWv3t7OWLCgO1xd7dm0OA+1WospU44jLi7V7G0YyBIRERFZieTkDIgiULmyA+zt9TWyRQtk\nk5JUqFp1NmbPDn2WWbQI55Etu3IP9lQe5pHV95F1clLC3V1XC1uUfrKpqWq0a7cKq1aFm3ncTEON\nbG764Fafrxfdvn3RmDr1BBYvPm/2NgxkiYiIiKxEYqKuBsnV1R4ODrrAr6h9Sm/ffownTzJx5cqj\nZ5Y/S+UM9sRAtqxRq42bFutraa2RftClChVs4eamm4O5KIHsyZP3cPp0LPbujSp03cxMDTIztYag\nNTd9jWx5aFq8e3ckXF1n4MaNxCLv48GDFADArVuPzd6GgSwRERGRlUhM1H3xrlzZ3tBHtqjT7+hr\ngkozcNQf285OAVtbfWDOQLYs0NfIKhQyKJXW37Q4JSWnj6y+RrYoU/CEht4H/p+9N4+Ss6zThq+n\n9qqu6n3JvkE6JAQSICxBIzDqvI4eEAdEfR35ZtBBQR1HXx3Gw1HPgXlxxu+MfsIrigrMoPMyKDgu\no4gCyhpIhHRC9qWTdKf36u7qrqquvZ7vjzu/Z6l6lvt5qrsrndzXOR5DVT9Vdz3rfd3X9bt+UPs5\n831nNZEllfZsILI/+9lBpNMF9PQMu/6MkZE0AODEiSnubUTYk4CAgICAgIDAAgEpsloi69ZaTBPo\nehJHIrJU7wswFUug/iDSqu0ju5Db75AiG42ysCfAnSL72muMyNK1aAW1LrfaWhyJ+OH1SmcFkd2x\nYxAAMDGRdf0Zw8OMyM6ZIpvP53H99ddj+/btymvbt2/HzTffjEsuuQR/8Rd/gSeffFK3zWuvvYYb\nbrgBmzdvxq233oq+vj7lvY997GP49re/rfv7eDyOd7/73fjwhz+MTGbuC7AFBAQEBAQEBBYKSAXS\nhz25I340sa+nIitqZM9caMOeJEmCz+dZ0Iqs2kc26FqRLZdl7Nw5BMCZImtkLZYkCU1NoQUf9jQ5\nmVEsxYlELUSWhTwNDaW4yyW4iWw+n8cXvvAFHD16VHntxIkT+NSnPoU///M/xy9/+UvceeeduOee\ne/DHP/7x9ECGcOedd+LGG2/EU089hfb2dtx5552m35FKpfCJT3wCsVgMP/zhDxEOh3mHJyAgICAg\nICBw1iMe1xLZ2vqungnWYho7q5H1nh7PwlX9ziZQ+x1qi+T3e+qecl0L9DWyRGTTjj7j0KFxhazx\nKLJEUqkethKNjUFlXPXCCy+cxOc+9wxKJXeLFH/605Dybx5yb4aRETWt+OTJaa5tuIjssWPHcMst\nt+DUqVO6159++mmsX78et99+O5YvX47rr78eN954I371q18BAH7yk59g/fr1uO2223Deeefhvvvu\nw9DQkE7RJeTzedxxxx3I5/N4+OGHEY1GuX6AgICAgICAgMC5Aq0iS2FPbq3FZ4Iiq7UWC0X2zEI+\nX0IgwNRYgCmzC1mRTSbz8Pk8CAa9GkXWGfEiCy3AzlO7a49sw0aKLMAIbr0V2e9850/43vfexMGD\n7oKatPtkctK9Iks1sgALouMBF5HdsWMHtm7diieeeAKyLCuvv/e978VXv/rVqr+fnmYses+ePdiy\nZYvyeigUwoYNG9DT06P7+3K5jM9//vMYHBzEo48+ipaWFq7BCwgICAgICAicS6CwJ5ZaXGuNLFNk\nz4Qa2VDIp4Q9iRrZMwOFQllJKwZIkV24RJba4EiShGDQh8bGoOMa2ddfZ/WxF1zQBsBelVVV4Ooa\nWfZ6EMlkHuWybPj+fODYsUkAcNS/VQvaJ4B7IivLslIjC/DXyXKFPX3kIx8xfH3lypW6/47H4/jN\nb36Dz3zmMwCA0dFRdHZ26v6mvb0dw8P6RKuvfOUreP755/HQQw+hq6uLa+ACAgICAgICAucaaOLc\n3h5RJo1uFUxSi86MGlm/YmUVqcVnBgqFkpJWDCx8a3Eqldcpo+3tYWVhiBevvz6AcNiHd7xjBQ4e\nHMfERAbLlzea/r22LtcITU0hAIzw0r/nE7IsK6TRDZGVZRk7dw5i5com9PdPY3LSnbV4cjKLfL6E\nrq4GjIykZ1eR5UEmk8FnPvMZLFq0SCG+2WwWgYB+BSIQCCCfVxv//vSnP8Vzzz2H7u5uPPjgg7M1\nHAEBAQEBAQGBsw7j4xl4PBKam0M1K7KkFtVTAdWGPVF41UK3FqfTeXz3u2/UVC94JiCfL1Uosgvb\nWjw9nUcspvISUkN5kUyynstbtixGZ2cDAH5FVvu9WlDtbL3sxSMjaaTT7P6hVUR5cfToJCYmsrjq\nqqVoaQm5Ti0mW/GVVy4FwN+CZ1ba76RSKdx+++0YGBjA448/jmCQHZRgMKgjrQCrhdVahzOZDP79\n3/8dfr8fN910Ex555BHcdttt3N9dSZQFqiH2kTOI/cUHsZ+cQewvZxD7qxpinzjD2bq/RkbS6Oxs\nQCgURHMzsyNms2VXvzeVYoQxkynWbX/l84wYNTZGFEU2n3f3e2YDhUIJhw+P48ILO6ve4x3TZz7z\nDH7wgzcBSPjc566a5RHOH4pFGX6/V/ndfr8XuZz9uTJXx+7kyQS++c3t+Od/fpcSdMYLWZYxPZ1D\nU1OHMr5YLIhUKg+/36/UAVuhp+cUZBm4+uoV6OxkWT7T0wXL35tOs2usrS1q+Hek1Gazcl3O+f5+\nVYWNxzOOx7Br1ygAtk/eeGMYiUS1iMmD8XFG5DdvXoRnnz2OkyenuD6nZiI7OTmJ2267DRMTE/jx\nj3+MZcuWKe91dXUhHo/r/j4ej6O7u1v5749+9KPYuHEjAODjH/847r//flx33XVYvXo11/dXEmUB\nPSoVcAFriP3FB7GfnEHsL2eo5/565ZV+LF4cxZo1Z1ZWgziHnOFs3V+yLGNwMIl169qQz+fh9TIi\nm0rlHP/eQCCAqSmmJmWzxbrtr5mZPCQJkKQSPB759GuFuo3noYfexN/93TN45ZX/B1u2LFFe5z2n\nfvvbY6dJLDA0lHT8O3K5IgYHU1i9utnZwOcAhUIJfr9X+Q0+n4RksmT5m+by2nvkkTdx//07sG3b\nctxwQ7f9BhrMzBRQLsuIRv3K+CIRH2QZmJqaUdwNVnj55ZMAgMsuW6SkbY+MWB9jstqGwx7DvyNF\nNh5PIZ+f/2N+6NCY8u+BgWnHx+6VV9g+ufTSTrS0BNHfP4VcLse1MKDFqVPMStzREcaqVU3o7Z3U\njcWM1NZkLS4UCvjkJz+Jqakp/Md//EdVzeymTZvwxhtvKP+dyWSwf/9+bN68WXnN61W995/+9Kex\naNEifPnLX9aFSgkICAgICMw1kskc3vOex/GFL/y+3kMREDDE9HQOmUwRixczNcjv98DrlWpuv1PP\nmtRMpoBQyKf0KfV6pbpanQ8cYALM7t2jjrednMzgjjt+o/tvp/jc536HCy/8ni5Ap16otBYHAvW1\nFo+OMvup04AmQK0H19aqRqOMHKVS9uRNlmU8/vg+BAJevO1ty9DaylqE2tnHjb5XC3qd/m6+0ds7\nqfxbmxrMi9deG0Ag4MWmTV1oaQkjlyu5up8MDTFluKsrilWrmjE1leO6fmoiso8++ij279+P++67\nD6FQCPF4HPF4HFNTzNd80003Yc+ePXjooYdw7Ngx3H333ViyZAm2bt1q+HmBQAD33nsvdu/ejYcf\nfriWoQkICAgICDjCvn1jyOdLGBhI1nsoAgKGGBxkk73Fi2MAAEmSEIn4MTNTW9hTsVhWbL3zjUym\nqNTGAiy9uJ7EmibzJ0/y1ehp8ZWvvIDBwRQ++clLAUDpN8qL/v5p/OhHb6FUkvHFLz5b1yRbwCjs\nyVvXsCfqoVwLkdXWqjohss8/fwKHDo3j5psvQHt7BG1tjMja1ciqRNasRjak+7v5BiUWS5LzsKff\n/vYYenpGcO21KxEM+tDSwn6Lm+Riuu4WLWrAqlVNAPjqZB0TWUmSFLn4mWeeQalUwt/8zd9g27Zt\nyv/uvPNOAMDSpUvxwAMP4Oc//zluvvlmTE5O6gKdjGTnyy+/HB/84AfxwAMPoLe31+nwBAQEBAQE\nXGHfPqbELPSAFoGzF6RaLF7coLwWifhdK7IURAPUL2CJEVnV1hkK+eoa9kSTed7UVC1efrkfTU1B\n/NM/XQvA+YT+gQd2olgsY+nSGHbsGMQTT+xzPIZK7No1rFPdnCCfP7Pa7xCBJULrBBTqpE0tJjsx\nhR1Z4bvfZQ7TO+64DAC4FVmj79XCTdjTsWOT6Or6Fn7yk/3c25ihtzcBv9+D7u42R2FPuVwR/+t/\n/R5er4Svf/06ANAQWefHZ2SEXXeLFkUVWz3PNei4RvbAgQPKv5966inbv9+2bRuefvppw/cee+wx\nw9fvuece3HPPPU6HJiAgICAg4Bp79zIrod0Ku4BAvUAkixRZgLWtcatgkrUYYESWFKr5RC5XRCik\nTkfD4YVLZE+dmsaqVc2IxQIIBLyOFNnJyQwefrgHS5ZE8dvffgRbtjyMu+/+I264oRsNDe6Oy8BA\nEu94x2O4+upleOaZ/+l4e1YjW0lk66nIzuj+3wmIUGqVUV5F9sSJBH7966O47LJFuPxyVjftRJGN\nRPzw+Yy1QzfW4v/+7yNIJLJ44YU+3HLLBu7tjNDbO4mVK5uwZEkMhw6NI5vVX49muP/+nTh6dBKf\n/vQWbNzIgtGI3LtRZIlEd3U1YNWq5tNjs78GZ639joDAuQJZlpFMJjE8PIiJiQmUSgu3p5qAgICK\n/fuZIpvJFF0rXAICcwnVWhxVXotEfK7a7+TzJR1hPFMU2WDQV9caWbfW4qmpLJLJPJYujUGSJLS0\nhBxN6L///V1IpfL47GevQHd3G/7+76/EwEBSUQLd4P77d9RULlEolKusxbIMlEr1UWWJwI6NubcW\nR6PaGll23tkR2R/8YBfKZRl33HGZ4iaNxQLw+TxcNbJmtmLAnbX4hRdYwFJfn3P7e+XY4vEM1qxp\nUVwePHWyg4NJfP3rr6CjI4KvfnWb8npzM/stblrwDA+n0NwcQijkc6TICiIrIOAQb731Fk70fgfR\nY/+Eqd5/xauv/gTZrLu+WQICAmcO9u1T0xudqrLlsoyf/eygIMACc4qhIUZItEQ2HHZHZLW2YqB+\ngU9GNbL1ItXJZE7XU9PJ9XzqFDs2y5Y1AmCTel4iWyqV8Z3v/AmNjUF84hMsEPX22y8BAPT0jHCP\nQYuJiQx++MMeAO6snqVSGeUya79DIFWxHvZiWZYVS/H4uBtFtrpWlZRuK2txqVTGo4/uRltbGB/8\noKp+SpKEtrYwl7XYzFbMxuNMkS2Vynj55X4ArKa6FpDlfM2aZnR1sXsKT53s737Xi3S6gC99aatC\nXgGgtbUWa3EaXV2MTM9pjayAwLmOYPG/cFliP1qLKaxL9+NK6Xns3+9+xVRAQKD+GBlJ61b5ndbJ\nPvfccXzkI/+Fu+/+4yyPTEBABdnv9IossxY7DQaqnDjXQwWVZRnZbCWR9SKbrY/TqbJGkGciTSBS\nsXw5s323tISQSGS5unBMTGQxMpLGddetVIhNR0cEgDv1EWBthEhpnJjIOj4/iKxqLbFkM66HvTiR\nyCqBZLXUyDpNLe7tTWB8PIP3vOe8Kstta6s9kbVXZJ0R2T17RpV62v7+6Zq6vJB197zzWhQSyVMn\nS2R3/fp23estLe6sxfl8CePjGeW+FosF0d4eFoqsgMBc4Lx0v+6/w6U8spPuVkwFBATODJAaSxM1\np4os2bEeeaTH9cRTQMAOQ0NJeDwSOjvVsCey5TpVMSsnzvVQZHM5RoiCwcrU4kJd2jDSBJ3stE7q\nZE+dYkSWFNmWlhBKJVkhUFagY6FVt4JBHxobg65q9mdmCvg//2cnmptDuOaaFSiX5SoF3g75PDs2\nldZioD6KrJa81lIjq60DJ2uxlSJL7ZgqSRvA6mQnJ7OmVutCgbWiaWwMGb4PqNZi3rCnF17oA8AW\nGFKpvONkbC1URbZFIZEUumQFbcKwFm5Ti6mtEpFpgOUA8NicBZEVEHCIYw3LdP+d8QYQaumq02gE\nBARmAxT0tGXLYgDOFVmaiGYyRXznOztnd3ACZx327h3Frbf+Aum0PcnRYnAwhc7OiE4lo+RVp7b2\nSiJbDzsvjVmryAaDPshyfcgSTZwvvXQRAGd1skZEFuCb1BPJ1LaGAYD29rAr0vbjH7+FeDyDO+64\nFCtWMJum07pFUl0rw57Ye/Ugsup+SKcLrs93rSJL1mIrRfbQoXEAwAUXVBPZ1tYwZNm8zZJd6x1A\nPeY8Cx4A8OKLrD72Pe85D0Bt9mJqvaNXZPmJLNmRCbxJzpWg79QS2YYGP1fJhCCyAgIaTE5O4qWX\n/gMzr3wKk9s/i6NvfBVvvvktvPLKf+Dxl17C8ePHkfP9Jd5o3oAJXxSHG5bhdfk6bNhwWb2HLiAg\nUAMo6GnbthUAnE/6tKTgu999s249AQUWBp54Yj+eeGK/UuvGA1mWMTyc0iUWAyzsCYDjXrKVE/t6\nKLL0nVoiS/+uB7GmCfWVVy4F4MxarNbIsuND6iqPYkbp0VqSBQDt7RHE4zOO1GlZlvHQQ2/C5/Pg\nU5+6zHVLFCKrekW2ftbiSqeLU+eLVWqx1YLSwYPs2XDBBW1V79klF5sdVy38fi8iET+XIkv1sWvW\nNGPrViaq9PW5J7JkLV61qgmLFlGNrL0KOjKShscjob09rHvdyTmvhZpYrC+ZKBTKtueaILICAqcx\nPDyM/iPfxDb5WTQXZ9CVn8KG1AlcldyFh5NF/G3Ci/cfGcSRqSksWfa3mF59NxrX/C9cffWHEAqZ\n20YEBATOfOzbNwa/34MrrmATWOeKLJuwvPe95yORyCohKwICRiCVjleFAZj1MJMp6upjAdVa7DTw\niYhsZyerxayPIktEVt9Htl7joQn1VVcRkeW3FpMy5kaRpWNRGQrU3h5BoVB2tDD22msD2Lt3DO9/\nfzcWLYpqVDLndYtApSLr1b03nyBFlvarU8s1qd5G1uJUytpaHAh4lSRdLewUSDOlvRKNjQGuY0z1\nsddcsxIrVrDzrBZFtrd3EkuXxhAO+xWbMI+dd3iYOUO8Xj2NpGPj9PlJ31nZHxuw7/EriKyAAIA3\n3ngFnuP/goumjmIg1I4XAh/D/qVfxXOe9+EPgS34aeF8FODBoZwXtya8+Py+PZAkD1pb2+D1eu2/\nQEBA4IxFuSxj374xrFvXpjzMnU6SaDX9rru2oqHBj0ceEURWwBxTU9nT/89PUIaGqlvvAKqC6dZq\nSfW29Qh7ou/UhujUk8hSfeBFF3UiFPI5VGSn0dERUcbvRJ0yStQFoCheTtTH73//TQDA7bdfCgA1\nK7La1OL5rJEtlcp4+umjSv0pEVmqVXWqyBqpo3bWYlmWcejQBNaubTXsA0tEthZFlt7nIbJUH/uO\nd6zA8uW1Edlcroj+/mmsWcMIenNzCMGgl9taXGkrBti1G4n4HdfI0nWn/cyGBr4FOkFkBQQALC88\nhWX5cfS0bECu/Q5s3fpunH/++bjyyg/gcWxCQXOpFOHBcGoCx479uo4jFhAQmC2cPDmFdLqAjRs7\nXNf4TE+zB/eKFU1YubLJ8fYC5xYSCTZpnQ0iS8qFW2sxEdlarMVf+9oLePzxvY63o+/UElkKfqqH\n1VmbCr1yZRNOnqxWZNPpfFW4jyzLOHUqqaixgFNF1txaDPAvrMXjM3jqqYNYu7YV11zDyiTcKrLG\nRJbNhSg9eC7xi18cxo03/hRPPLEfgEpcyeLrpAWPLMsK4dOqo3bW4lOnkkil8oZBT4BqLTa735sp\n7ZVoagpy3QtefZWVImzbtkKpfXbbS/bAgThkGVi3jv02SZKwaFHUVpFNp/NIJvOKk6MSTvsnA+q9\nTRseRURWKLICAhw4VHg7nvO8Cxs3/SPWrFmjvN7T04PH4j7IkJTXYsjj3uAOrAgcxejoaD2GKyAg\nMIugxOILL+ywrXkyg3YiGgh4lTRWAQEjkCLrJEnWqIcsoCWy7hRZavPiVgHNZov4539+Fd/5zp8c\nb2tUIxsKMeJUD4V4eDiFaDSAaDSAVauaMDGR1Sll8fgM1qz5Dr7+9Vd0242PZ5DNFpX6WABobuZf\nFDMPe3LWgudHP3oLuVwJf/u3l0CS2LzFvSJrFfY09/e3wUF2vr/1Fptn0T2ZQpecKLL/9V+HsGvX\nMP7H/1ijI+ZElsysxVb1sYBWkTUmbjRGeq6YIRYLIp8v2Z7zBw7E0dISwrJlMSxa1ACfz+NakX3z\nzWEAarAZwMKWhodTljXZamJxtSILuCOyRuFRvCUTgsgKCADYtu0vsW3brfD71TqdwcFBfGIigxz0\n1uH3h0ZwqX8aS3Jx9PefmOeRCggIzDa0qZRNTUF4vZKr1GKPR0JDgx/BoLcuk/AzDZlMAUePTtR7\nGGckaKI3O9bi2amRdauA0mTdTjkxAilhRMaBeluL04oqtGoVs1xq62R37BhEIpHF7t36RezKxGIA\naG2dnbAngH9h7Uc/eguhkA8f+9hFmnG4VWTr236H6sePHmXJutWKLN8+SaXy+OIXn0Ug4MW//uu7\nde+pRNZYkaXWO2ZE1k6RpcWnJUuMSR+Besla3Q8KhRJ6exPo7m6FJEnwej1YujRWM5G95BK160ZX\nVwMKhbLl808lnQ2G77e2hpFImLckMsLJk1MIhXw6wi8UWQGBGlAul/Hvf9qBQwX9QyWIEj7esBTH\nQl2IlPMoFEQdnIDAQgdNHtrbI5AkCa2tYReKbA6NjUFIkoRg0IdCoVyXPphnEr761RewadMPHFkA\nzxXQOeckxGdw0Mxa7I74EXmqtUaWahfdEFlKXNUqmSqRnV9XQ6lUxtjYjKI0rVzJrJvHj6vWzd27\nWc/4SnVTTSx2ay2ubg0DOKuRLRRKOHgwjksu6VLIq34cvGR4Dz7wgZ/ij39k9Zj1ar9D+4QWw+Lx\nGYTDPsVSy6vI3nffKxgYSOKLX7wKa9e26t7z+70IBr2m1uKDB81b7wD2qcV0zS5dGjN8n0DH3ep+\ncPx4AsViGd3dKqlesaIRg4NJVwp5T88w/H4PLrywQ3mNzn0re7EdkaXacN5FunJZxqFD4+juboXH\no7ofeWtkfZbvCgicoxgeHsZ/h1uBiufPVxcBV19+NXbu9KCM72JT8U288tT/q7wv+/xoPu+d2Ljx\nIggICCwMqGmW7MHZ2uq8d+P0dE5ZVScFI58vKfV+5yIGBpIoFssYGkqhrc24nupcBal0TogshbBU\nt99xp8jSeV9rjSwRCqtenGY4fpypnatXtyiv1av9zujoDMplWZmgU0qttk62p4cR2Up1kxTZ5cu1\n1mLnYU9m1mKe+9GJE1MolWScf76erKmqIZ8i+2//tgcvv9yP3/zmKID6WYtpoaW3N4FyWUY84tgI\nwwAAIABJREFUPoP29ohihbfbJ5lMAd/61uv49rd3YOXKJnzpS1sN/y4aDZhaiw8dGofHI1URYIJd\npgLZoysXnyrBQ2QPH2aEvrtbHcvy5Y2QZXavJQcBDwqFEvbsGcWFF3bonlHkRhgeTmPDhg7Dbek+\nZGYtJifCxERGt6Bihr6+KWQyxSrVmze1+Nx9wgoImECWZTz21BNoy0rwrr0cJY8HkCRcGMjiI8u6\nAQCXX34V3tj9Ii5N7MN17W8p25YhoTDxFgqF7+lsygICAmcu6EFJwR9tbWEcOTKBclnWrRBbYXo6\nr6hKwSDV+J3bRJaIEU1IBRgymYJSQ+3UWuzxSFUhK0T83FuL2eTVLXEcG2MKjRsi29vLbKOUnAqo\nYU/zTWQpOZUm6KtWMeVPm1xMRHb2FVk7a7E9kSXl8rzzWnSvNzaycgleRXZkJI1YLICLLurEq6+e\nQkeHqrzNr7WYnZ/ZbBGnTk1jbGwGF1zQjpaWEDweydI18+abQ/jQh36Gvr5pdHU14JFHrtfZ17WI\nRgMWimwcq1c368LItNCSNiMMDaUQCHhtCR2lVVvdK1UiqxK+5cvZOdrfP+2IyB48OI5crqSrjwXU\nGlWr5GI7Rbalhf1W3jpZM9VbXaCzvq+cu09YAQETvPrKH7D7RAIxjwc3TDyHEytWYd+SNbg1XMai\nTm0twa3448izQEZzwXt96Fz7dqwTJFZAYMGAJuBEZFtbwyiXZUxNZZWHshVkWT6tyLIHMSmy53rg\nExErCjYSYKDEYsC5tbirq6Gqd6NaI+s8tTgQ8CpOAvdElk3ic7kSisWyYZsSMxw/nkA47NNNiutV\nI1uZnErWYiKIk5MZpV7WTJHVEtlwmNXL89XIGiuypD7y2GiPHWOLAuefryeykiShpSXErciOjqax\nYkUTnn/+r7Bv35gusXc+FVltj+U9e0aRyRTR3h6G1+tBa2vIcp984xvb0dc3jb//+ytw991vt2x/\n09DgN7TSjo3NIB7P4Morl5pu6/d70dgYNCXVQ0MpLF4cVYK3zMdgnZ4MAEeOMMJXqcgCzlvwqPWx\neiJLiziU3m0Ees+o/Q7gbAEH0BJZvSKrWout7wOCyAoIaDA8PISfPPMHFIsyAgEPPIUy1vQex+bR\n43j3zTfresYuWrQIixb9VR1HKyBwdmB6Oof77nsFd921lYs4zjZowkSTSFplHx/PcI0nnS6gXJaV\nFguqIrvwA58SiSy++c3X8elPbzFdgTcD9TXVTkgF9FZTXiIryzKGh1PYsKG6Vo+UCzd9ZBsbAzUT\nR63FM53Oo6kpxLWdLMvo7U1g9epm3USfxjPf7Xcq01jb2iJYu7YVL77Yh5mZAnp61ICnbLaITKag\nLCKcOjUNSaoO9eFNcE0mcwiHfbpEXYDdk/x+D1fNPoUiVSqybBxhLkU2my1iaiqHSy9tgCRJ2Lix\nU/e+zzf/iiwAvP76AACV2Le3RyytxSMjaXg8Er7+9T+zddVEowFlEUALSiw2a71DaG0NGyqypVIZ\nw8MpXHHFEsvtAb5go8OHJ+DxSLrj65bI7tplRmTJWmylyFa3ytHCaU22NmxRi0jEntwDIuxJQECH\nXbtewI5DgxgaG0ehUIQkSZAAJFPAi6+/hLGxsXoPUUDgrMMvfnEI3/rW63jyyYN1+f50Og+PR1Im\n0E57yZI9lJQtskbm8wtfkX3mmWP4l395Fb/85WHH2xIRcWKfPRegVah5iWwikUU2WzSstaOwJzfW\n4lgsWDORJWsxYN7GxAgTExlMT+ewZo2eeNWrRtbIMnnjjeuQyRTx+98fVyb/tL+0Cmd/fxKLF0er\niGhLS5g7tdhINZQkCR0dES5FlpTjyhpZgC3OTUxkbQPo6Hu6uoxr2utRIwsAr73GiCxZrdvbIxgf\nz5gm446NzaC9PcxVGtLQEEAuV6r6TWZKYSXa2kKGCw1jYzMolWQsWWId9ARo+9laEdlxrFzZpCtX\nIdfAyZPOesnu2jUMr1fCRRfpFyqIhFrdl0ZG0ggGVSdHJegzeB0Ahw7F4fFIVU4C3vuaILICAqex\nd+9e/O9Hf4tCCcjm8kB+CiGfetN/9eAwfvHLx1Euz/1KpIDAuQTqwVcvwpNM5hGLBRRVyGkv2cqm\n92eTtZhssE5Jknab2Tiusizj1lt/gW9/e0fNn1VvaBU63n2j2l6NiCwpss6txY2NwZoVUC3JclIn\nqwY96Wv76qXIGu3j97+f5WL84heHFCJ79dXLAKiKU7ksY2BgWmcrJpAiWy5bE8hkMldlKya0tVmr\nj4RjxybR2RkxJMQtLWHk8yXb63h0lJF5bV2sFnRvm6/UYjq3d+4cBKAnsrJsTpYYkeULmKOQv0oS\nSf1rzUKPCK2tYWSzxap9yxv0BNi3AUokshgdnakKnaJcBieKbLFYxu7dI9iwoaOq9pcItdV1zFpU\nmdulycXEs4ADsAWD1aubq/IkVLu1ILICAgpSqRT29LyOnS//Xve/HTv+gN/+7j8xnWETz66mAL74\n1x/GZz90I9YtboAkySjLEv5wcBKP/NtDSKXMbRcCAgLOkEiwCaHWSjafSKXyygMccK7IEpE1Si1e\n6KBj4ua3EBGZjeOaTObxxBP78dRTB2r+rHpDS15zuRKXBZ3q0ozsfGRvdWItLpdlJJN5NDYGalZA\nK63FvDAjsjQep1bpWlFpLQaAyy5bjGXLYvj1r49i584BRKMBXH45s4oSiRodTaNQKBsS2ebm0Ol9\nbX0NmCmyACNtyWTe8jwpFEo4eXLK0FYMaEOJrMmFXZDP/NbI5rBiRSNaWkLKvYTIKS02GhH8QqGE\nRCKr2JDtYEbeenqG4fPp29MYwazdjNouy16RJdJmttBw+HB1fSzAFk9bWkKOiOzhw+PIZIpVQU+A\nPZGlEgerMhO7ACwtxsZmMD6eMVS9RfsdAQED9PQ8jSvwe0RL+pu5PAOsPh/40wEfBpIe3P2JD+Gy\ny66E1+tFOBzGj//rp+gdZzfuXSfGsH73drztbe82+goBAQGHINXPSfDNbCKVyiuTEcC9IkvJk2dT\njSzVt7r5LbOpyJJSdDbU25JSIUmALDMS09FhPR2j1FojlclNajFNVLXWYrcK6OioVpHlH0NvLyOy\n2sRiwL3CXCtGRlgqNPVuBQCPR8L113fju999A4lEFldfvUy5P5AiOzDAlDejXqHa4Buz2uFisYyZ\nmYIpke3oINKWMe1HatZ6Rx2HOmaqqzQCXWeUZF2J+ewjm0zmsXp1M2KxAHbuHAKgnv9WLXji8czp\nv+Gr6ScSqT13S6Uy9uwZxYYN7aaJxQRS0ivJHyn8lXXTxmOwVmSNEosJy5c3orc3AVmWbUOlADXo\nafPmrqr36NozG8fkZBaFQtmSyNKzlKc2nOqQjfr08t7XhCIrcG7BpD5EkoClTcC3b4rif//t+3D1\n1dcqwU7r1q3DP3z6c3j7Bc3Y1J7BP17Whwuk/57PUQsInNWgib2b9h2zgVSqoLP1uVVkaSKqbb+z\n0KESWfeK7GwsUNCEtV7nyGyCzneqneNJdVYn59VEVm1TwU/8tIsvPp8HPp/H9cKLlky4sxYb18jO\ntyI7NGScCn3jjeuUf2/a1FWlbpLyZlQLydNL1qyHLIF6MFu14FHrY2tTZMkmXtniiUD2z7m+t+Vy\nReRyJcRiQR0514Y9AcaLjVSzrV2QsIIatKSeu4cPTyCTKRqSvertjUOJhob4rcV2NbJGPWQJy5c3\nIpXKc1t59+5lWS+bNlX/No9HQkOD33RBSlXszX8TPT/5iKx5HTKvtVgosgLnFDZt/gscOLocuZSx\nDaNj3RpcvXx11euxWAwbu9dh2+o3EfDKeDVwEazL/wUEBHhBD+B69BstlZgaorUWO1dk2bjPxrAn\nmmQ7nbiWy7JiVZ0NIkuq39mhyLL9sWJFIwYGkly/icgikRotVCLLT/zUpG52zoZCPmSzzs/XbLao\nG79Ta7Ekqf1aCapVev4U2ZmZAk6dSuLiizur3nv725ejrS2M8fEMNm/uUtRNWuiihNfFi6tVKp5W\nJGY9ZAk8LXisEovZOPQqshmIqJgpsvNVv0znVGNjUEfOiZzS/xvtE5WM8ymyVCOrXYSheujNm6vt\nt+bb668/VZG1txbTNWx2/ajWYmNFFmB1sjwp+319LBiq0tJPiEYDpgtSqv3efN82NgYhSXw1sqoi\nK6zFAgJciMVi2HTJVtP3A4EA8vnqCziXy2FF8AUEyjImvQ2Ixd4zl8MUEDinoBLZ+bcW02qvcY0s\n3wo3WWfPxrAnt9ZirZo2G8eVJqfT0zluC92ZClJgV6xowvbtA1zWayKyRoqs3++B1ys5UjArXQTh\nsM+VAkrHhWzSTqzFx48nsHRprMq6qZKl+VNkX3mlH/l8Cdu2rah6z+fz4P3vX4dHHunBlVcuVUgp\n/T8pb0ZBXLOhyFqpjwS1h6yxtZhXkbWzFqv11HN7bLSOAe1v0oY9AcYqNV0rvGFPqrVYnfv19IwA\nMLbfVoKeHZV10GQ557EW8yiy0WjA8LOWL2cLQX1907j4Yvvx9vdPw+/3GJ6vACOQZoSaWu9YWYuZ\nqhuwVVIBtfXOunXVRFYl98JaLCBQM3bs+BnWZlj8+17/+Vi5cmWdRyQgcPaAJoT1CHsiolZL2BON\n+2wMe6IJpdPfolVsZqNGlianpZI8721ZZht0vq9YwSagPPuHSAy5BbSQJAmRiN+RSlZZ1+1WkaXj\nQrWbvNbifL6E/v5pQ1VItRbP33F+/vkTAIB3vnOV4fvf+Maf4dVXb8P69e2a9iLsmAwNVYdEEYhA\n1qLIWqmPBLIW16rIkvPBzFo8X2q51jFAiqzXKykLA0RSjfYJ/QanYU9awtTTMwxJgqFCXwkzC+zQ\nUArRaEBZ4LT+DPPa1FKpjKNHJ7B2bavhAt7Klc56yfb3s4Rts9ZETJE1Jo8UOmdlLQZw2p5sfy84\ndGgcixdHDevHAwEvfD6PqJEVEKgVhw8fxtvLzwIACpIXnZ1/iVCIr+G7gICAPWgiXw/bKD1stUQ2\nFPIhEvE77iNbXSO7sAkXoO4fp+qydvIxO9ZitVfpQrcXk7WYLIE8Czg0YTcisgBTL2q3FrtXZImQ\n8o7h5MkplMuyIZGtR9jT88+fQCDgxdvettzw/VgsiK1b2XuVNYCqtdhckbVSQivV8UoQabNqwWPV\neoeNmY3DrlxidDSN5uZQVSsUwnwtMqjnp6rItrdHFPJltU+s3AtGqLQWy7KMnp4RrF3bykVCrcKe\neOpjAWsbbV/fNHK5kmF9LKDeR8gybIVcroihoRRWrDAP/CJrsVHPYR5rMWCt6hJSqTz6+qYN1Vjt\n5wgiKyBQA8rlMsbGXoYfRZQg4UXpRqxeXV1DKyAg4B6qIntmEFmAEQb3YU/zE4gyH3Ab9qQNHppN\na7F2TAsVU1NZRCJ+ZaLNq8g2NgZNCUYo5KuLIkvBOqtWMULKq8iatd4B+MlSNltUwmJqQTw+g56e\nEVx99TKFRFuhUpEdHk4hFPIpjgz939r31OS1FpsRWWq9Y2Yr1o7DLoBndDRtSQDnq0ZW25u7uTmE\ntWtbdXWUpFJTCJoWdK/g7yOrV1SPH09gairHZSvWbq899/P5EsbGZrhsxQDg9XoQCvkMr5/9+1k4\n0/r11cm+gGot5lFkT51Knt7GnMg2NPh1GQdakLXYrv44GrW3FpOLwIrIRiJ+YS0WEKgFmUwGm7ED\nHsg41rAUGza8rd5DEhA4q5DNFmc1FMgp1DYk+klka2vYRfsdvSJ7NliL3dbIatW9qamc4eq+E+iJ\nbH3aNM0WEoksmpuDyjnHc96Pj8+YqrEAO+ecHKPKczYU8rqskWXXCAU28RLZ3l5W07lmTbUV1u/3\nctX8fuMbr+KSS36g1Ie6xR/+cAKAua24EoGAF9FoQKfILloUNbR9zkbYk0pkje9H1HrHzFYM8PX2\nLBbLiMdnLOsf56tGlq5xWmh5/vm/wn/+518q74dCPni9kqFaR/cKXkW2MnVYrY+1D3pi21fXcjoJ\neiJEo8akbd8+RmTN+tkuWtQAn89jSGRffLEPS5b8fwoZpr+hsgbjcVS3IyKo1mJ7RdZM1SWcPMnG\nQotgRuBxmggiKyBgAZ/Ph1PhxRgItmIwfx1aW0VWsYDAbEKrVCSTeZTLtREepyCiRpMZQmtrGOl0\ngYscqNbiyj6yZxORda/IFgrlmvfF2aTIJhKspygpeHaKrCzLGBubsZyYB4M+R/u40locDvuRy5Uc\nLziQSkjKKk/AC2CtyNJ47FS/7dsHUC7LCil2C6qP/bM/W8W9TWtrCJOTGZRKZYyMpA0TiwG+sCet\n+mgENUXdWJG1a72jHYcVoR4fz0CWrdW2+VNk9eS+s7NBsXQDal24EcmJx2fg8Ui6v7dCZeqwmljs\nTJHV3pectN4hNDQEDH+PHZH1ej1YujRmSGR37BjE+HgGv/vdcQCq/dhKkTVSmAlDQyk0NQVtnQsN\nDQGUSrLlPam/n42FanyNP0cosgICNSEYDKJj1Wcgr/lHXHnlny3opEwBgTMRVC9ImO8+obQKX6nI\nOmnBk0zmEAx6Fdun3392EFlZlpVJtlNFtnJCVmvgE1lYgYVNZGVZRiKRQ0tLCI2NjFzYKbLJZB6F\nQplDkeU/3yqtxW4XX+bCWgwAkYh9ijJN8K1CkOwgyzKee+4EWlpCuOQSPgUOYFbdiYks4vEMSiUZ\nixcbK2+qImt+H6lUHyvh83nQ0hIy/Z1kr1671txa7PV60NwcslRk1cRi8wWTetTImsGMyI6OptHe\nHjYNM6pEJXFzklhstD2gKrLOiKxxQNK+fXFEIn7TawVgrbwGB5MoFPTXL11DlYqsnbUYqG4FJMsy\nTpxIYOVKczWXoNq1ze8HfX18YxGKrIBAjWhtbUNn5yJ4vd56D2VB4sknD+Dzn/9dvYchcIYikdBP\nrOabpNAqPK3KE3iseISpqZyuPk61Fi/ssKdMpqgo5LWEPQFqyxk3KJdlna1yvhc7ZhOpFHMdNDUF\nFeJiR2R5av7IWsyrqFaqgGoarTPLKI3NKZEdHEwiEPCaqszhsN8ynXpsbEYJnqGUWjfo7U3g5Mkp\nXHvtSni9/FPilpYQUqm8oiqZhd+EQj6EQj4bRdbaWgywY29WI0u9ODdsMFbstGO2UmTtesgC85la\nbK1S01iMxhGPZ9DRwddDFqi2Fu/ePYIVKxoNezYbwYi0DQ46txYbqY+FQgkHD8axYUO7JTFfvrwR\nsqy2/CHQfZgWfZxZi/XXcjyeQTpdsLQCa38L+wzz+4lKqs3HEg77USyWLct0BJEVEBCYU/zgB7vw\n4INvLPi6NoG5QeXEar7rZM2sxTSp5JmYJ5N53YTrbAl70h4Lp7+FSAhZ0GpZoEgksigWy4bjWmig\n8725md9aTJZSKyIbCPggy9DtJyuopIV9ZijEFl+cBj7F4zPw+z1YvDgKSeK3Fo+OslpMM5dTKOSz\nVGJoYg7o1XqneOmlPgDAtdc6a6lHttX9+xmJtFLemA3ZfdgTwMKNxsczhqUXBw7E4fN5LGtkaRx8\niqw5CfT7PfB4pHntI2uGcLj6HMnnS0gkstz1sYC+JnR8nC2QmNl47bYnuFNkA8jlSrpr+OjRSRQK\nZWzcaD0eIqaV9mLaP/v3x1Euy1zWYjWFWb9vjx9nFn6qh7f+LeYpzIT+/ikEAl7LelszdVgLQWQF\nBATmFDRJW+iTeoG5QWXrmvle8DCzFvOsKBPMFdmFfc5ryadbazEpVbVYiyt7Wy5kazHth+bmECIR\nP7xeyfacJzWaklqN4NQaPDycQjDoVWon3Sqyo6MzSluUhoYA18KPLMsYGUnbWlitVD89kXWvyB45\nwupLnRAXQLUM0ziMesgSGhuDlosvPIpsZ2cDSiW5SpWVZRkHDoxj7dpWpX+1+ZjDyGSKumM8NjaD\nl1/uB6C9zsyJhSRJtsdmNlBZw20EZj/Xj4P2D29iMaDv4XrokH2SbiXIzaO9jgcHmTLqVJEF9KRt\n795RAPZqOxHTSiJL+2dmpoATJxLo65tGe3vYssa1UqEmnDjBSDCfImteZ0tg/WxjlkqzSojNzzdB\nZAUEBOYUZKmysokJnLsgpYIexDSpmy/QhKmy/Y5Zk/tKFItlzMwUdJPQsyXsSTsJcWstpgl+LSoq\nTU4p4XYhW4vpftjcHIQkSWhsDNqSfPr9VlZHp72Lh4fTWLw4piiibs/ZeFwNoYpGjWv8KjE9nUM2\nW7QkTJEIs42aWaX37lWJrLbHsFP09rJaXTs1sxKViqw9kTXfL3Z9ZLXjO3pUH2w1OJjC9HTOtDWL\nfszVgU/33fcy3vnOH6OnZ1jZj11d1iRwPogszz6hGlntOeK0hyzAfg9zE+Rx6BCrN3ZCZEMhHzwe\nSfesIGux0xpZQL94ahf0RDDrJatVRPfuHUN//7SlrRgwtxafOGFd167/DH1v3kpks0UMD6dtx0KE\nWyiyAgICdYMaFrOwJ/UCcwOqnaQG7fOtyJr1keUJqwCMJ1ykjDhVMc80aMmnU3WZJrpkG6uFyJJ1\ndM0aNoFayIoshZs1NTFSYafWAVpF1p7I8liDy2X5dNKuOslW02j5FdlstohkMq+Mq6HBvnckoNqa\nrSyFoZAP5bKMQsHYKr1//xi8XgmBgNe0dpQHvb2TCId9jggHoCqyVJ9qtX00GkA+XzK9HySTOfj9\nHuUYGoGCnEhBJhw4wL5//Xp74mXUS5aOxRNP7FeIrF19qV398myArnFrazEjOdqxOG29AzCVORoN\nIJUqKET2ggvsFwa028diejfC+PgMYrGAcl3xwOiZs28fO7681mIKUCJor8eXXupDNlu0tBWzcRi7\nkY4fZySZh8jaLQSfOmUf9ASoRNbKoiyIrICAwJyBJXSyh+ZCt1kKzA0qFdn5D3syU2StV5QJRrVc\nRGQX+jlfi7WYCBEpVbNhLSZFdiHX21O4GVl6m5qs1TpAWyNrZS2mumz74xSPz6BYLOuSdlVSwH/O\nVpIGRgbsr18eCyul4xpNYGVZxr59Y+jubkNXV4PrsCdZltHbm8Dq1c2OOxKQIkvEwU6RBczvbdPT\neTQ2Bi3HYE9k7YmXUQseOl5PPnkAw8NMRbTrERoK2SdK1wqV3JsTQSOS44bIAuq5SwsT69aZJ0Bb\nbU9IJLLKYgcvVPVRq6KOoq0tbHtMli1j17JZjSwAPP30MQD25NHMFkxJ4zypxXa1rWroFN9YrBbI\nBJEVEBCYM8zMFFAqMduPsBYLGIEIDj0c5zvIhx7W1TWy5k3htTBSZM+WsCctYXTbR5ZqZGfHWnz2\nKLI0yW1sDCKZzFn2T+ap+1NdAPbHySiIRg174r9PV9o4o1GWumqXnMwTKmSVjtvXN41kMo+NGzvQ\n3h5xXSM7Pp7B9HROWSBxArLpAqw9jlVrJLq3mJ23yWTOshYUmB0iS+PQkgsiOn1903j55X5EIv6q\nRb1KhMM+x6FgTkHk3gqRCC121KbIAmqbl0OHxtHREeFOLNZuryVbExNZZeGAF6oiWzj9/3kcP57A\nxo0dtgstsVgQLS0hgxrZAnw+DyIRv3LuWKUE68dRbS3u6mqw7SGr/Qyz56faesfOWmy+oEUQRFZA\n4DReeqkPN9/8E0G4ZhHaHqEL3WYpMDeotyJrViNL9qqZGTtFtjqo5ewMe3JXI9vVVXuNLBGf1atJ\nkV24RJas9BQO1tgYhCxbq8xOwp54zjkistogmlDIedhTJWloaAigXJZtn6FkZzVrWQOoE1ij8VDd\n4MaNHejoiGBmpmBbAmCE3l5Wb0oLJE5ANl2A/Q6rwBqVyBofY0barMljV1cDYrFAFZE9eDAOj0ey\n7CFLUFUydZ9qiUYmY123TGA1snOvyNqRe1rs0JIcN2FPALv/T05mceLElKP6WO32dF8qFEpIpfKK\nas+LShfQgQNxyLJ90BNh+fJG9PdP6xaSZmaKiEYD2LBBXehYudK5tbhUKqO/f5orsVj7W8wVWfv0\nZEBYiwUEHOGppw7iqacOKI2jBWqHdvI61yu4AgsTZD1ftozCnuY/tTgU8sHn0z8O7VaUCURMzsaw\nJ63l1UmPUkBV9mZDkVV7lbJJ1EIOe9K23wFUQmtlLx4fn4HXKyl1tUagWjyeBUOykBopsk7OWapd\npppK3qRvHkWWfo8RKSYiu2FDh5J87MZefOwYEdnaFFkrWzGgJu8aLcCUyzKSyZyt+ihJjKweOzap\nqPeyLGP//jjOO6/F0oJLMArxSafzaG0NKb/HKkmaEA77USiUUSrxtXpyAx5yT/ZzLamm88BJH1mA\n7ZtslvXN7u52R2SZA62sLOA7VWTJBaT2fuWrjyUsX96IVCqv61k8M1NAJOLThUW5sRafOpVEsVjm\nqo/VfoaZJVhYiwUE5gD0ADcLlxBwDu0NVSiyAkZIJLKIRgPK6nU9UotpBVoL3hpZmpxq2++cLWFP\nqRSbkAUCXkc9SoHq1OJaamS1FtZYLLCg+8hq2+8Aqlpn9ZvGxtQWN2ZwsniiEtnqGlknShsls2pr\nZAH7a0btYWtvLTZSYvSKLPsMN71kKbG4dkXWmsgSITM6xul0HrJs3UOWsHZtK7LZohKUMzo6g8nJ\nLJetGDAjsgU0NYXwgQ9cAMD6mBDUYLC5ub/JMh+5J7VOOw71XuFODQWACy5wR2QBdr5OTjIHhdMa\n2cpnjnbBhgdGvWQzmQLCYb/uM9xYiymxmKf1DvuMavVfi5MnecOehLVYQIAb+Ty7GQoiO3vQTl4X\nujolMDeYnMyiuTmoTFqIPM0X0ukCotHqCVPl6rgZKvvgAmqN7NliLSZLq5NreDbb74yOzqC1NQS/\n34tYLLCgrcXa9juAml5sRfTHxzOWNZiAs7ps6xpZ/mO8Y8cgAOCSSxYBsLcTEnhSi1W1rZos7d07\nhoYGP1atalZItJs6WbIWO229A+gVWbvEYyuCz9NDllBZJ+ukPhYwthaz+18AH/rQBgAZglyXAAAg\nAElEQVRqaJAVrI7NbIDVWVeXe1TCOOwpDa9X0i008ED7Xe6sxaobQb3Ga6uRHRhgvWh57bxqCx6V\nyKbTBUQiflx4ITtHgkGvrepuZC2moCdeImvXR7a/fwodHRFlwcoMkYhQZAUEuEETACeqg4A1yHYJ\nLHx1SmBuMDWVQ3NzSKNMnRmKrF0fPHX7aiLr8Ujw+TwLfvGGCCMFnzi5hmmS29ISgt/vqbn9Dilv\nvMm4ZyoqFz5Ua7Hx/ikWy5iczNrW/DlxAahE1r0iK8syXnvtFJYtiykTaF5FdnQ0Db/fY6lYGalt\nANsfhw6NY/36dng8kjIpd0dkE/B6Ja4U1kqEw35FmbQjsnSsje5tdNzt6kEBKyLLR7yMjk8qlUdD\ngx/veMcK/N//eyP+4R+utv0cI0vvbMIoCd54HERk9WFPdu4FI2iJbC2KbCqVx8QEm/e4Ty1mx8dp\nvS8tQhABBqqtxcuXN9oGRxmRUFWRrb1GVpZl9PdP26qx2s8RiqyAAAdIPSkUFvbk80yCUGQFrFAu\ny5iaYm0KaCIwn61VZFlGKpU3XPm3W1EmGCmyAFv5PnsUWSKy/L8nkynA45EQDHrR2Bh0bS0ulcoY\nH88oyttCV2QnJ7NoagrC62XTLztr8fi4fQ9ZwFnY0/BwCj6fR/eZTvrQAowEjo7O4Kqrlimv8daV\nj4yk0dnZYDmhNrOv9vdPI58vobubkTr6DW4V2eXLm+D3m/dvtQKpsnbWYvXeVn3eqgthfNZiwL0i\nW0lk8/kSisUyGhr8kCQJN920HkuX2iuyajDY3CxOE+G3I/dGgWDxeMZx0BOgEqZQyKdYdJ1tr+7b\n2VJkR0fTaG4OKYtUdlDbPLFzqlAooVAoIxz2Y/HiKK69diXe9761tp/j83kQCvkqrMX8PWSNfosW\no6MzyOVKXPvZyEVQCUFkBQROQ9TIzj60k1eRBi1QiampLGSZ2Ss9Hul0/eP8kZRcjk3kjIhsMOiF\n1ytxW4u1NbK0/UJfvCFyRfVmzqzFRYTDPkiSdLrFjLvjOj6egSxDQ2SDyGaLdV1w/OUvD+Ozn/2t\no/ArwtSUvi0HnTdmRJ96yM6mtXh4OF2VtKuGK/GpbK+9dgoAcNVVS5XXeKzFsixjdDRtW4tppvqR\nHZgm1PQ5FCDFi3Q6j+HhtKv6WALZV63Sl4FqgqGFE2vx+eczCzQR2V27hiFJ4A4nqiQFRGiJhPGC\njs1cPdONXC5GqLQW5/MlJBJZx613AHUfdHe3OlZzAb2DhwLdtPZzvjHoXUDx+Iyj31IZKkYLDbRQ\n8cwz/xPf+MY7uT4rGvXrFqROnGDuBQpltINVxgRvYjEwB6nF+Xwe119/PbZv3668Njg4iNtuuw2X\nXHIJ3ve+9+HFF1/UbfPaa6/hhhtuwObNm3Hrrbeir69Pee9jH/sYvv3tb+v+Ph6P493vfjc+/OEP\nI5PJOBmegEBNEER29iEUWQErmPXUnC/QQ9aIyEqSdNrGaj2xp/FWhrUEAt6a7PQ///khfOlLz7oi\nS7OFVCoPn8+DxkZ2fJwqsjQJaWpyr8hWtnix68k51yiXZXzxi8/i+9/fpbPw8UCWZUxMZHWLHqrt\n1Hj/8PbF5A17kmUZQ0OpKhVRJSd8x3j79gEAwNatKpHlsRanUvnTbV6sf49ZHSbV6lHSsNsaWTXo\nyXl9LIGIip212OqcNbt/GKGpKYSurgYcOTKBHTsGsGPHIK67bhVXX0+g+vio9z++7QlzbS2m/WS3\nT2gcRHJ43QtGoH3gxlbMtqeMB/eKrDaht1yWEY9nHBJZ/XlG+4X3/Kgci3ZB6vjxBJYvb6pK9zdD\nOOyHJBkrqRRGNe9ENp/P4wtf+AKOHj2qe/2OO+5Aa2srnnzySbz//e/H3/3d32FggN3ghoeHceed\nd+LGG2/EU089hfb2dtx5552m35FKpfCJT3wCsVgMP/zhDxEOOyvWFhCoBRT2NJeR8uca9KnFgsgK\n6EHnB03s59s2ShM5swlTQ4Of21pcrcj6XFuLjxyZwF//9S9x//07lWTYemB6OodYLKCxrTqrkaVJ\nSGNjEKlU3tW99Uwjsi++2IeTJ5mi4LTud3w8g1Qqr7PU2RFZmpzbK7J8NbKTk1nk86UqImvV7sYI\nr78+gHDYh02bupTXeKzFag9Za/Kn1shWKrLGRNapIltLD1kCqcFLl9r15TQvm3CiyALMXnzixBTu\nvfdlAMBdd9nXtFaOg+5pRDLcKrJzZS3mJfdqXTcbh9m9mAe0D9wEPQH6lF5SZN2mFs/MFDAxkUG5\nLDsispXHtxYiq80iyGQKGB5Oc9fHAiwnIhLxGxJZCqPiqU3ncXlwEdljx47hlltuwalTp3Svb9++\nHSdPnsS9996L8847D7fffjsuueQSPPnkkwCAJ554AuvXr8dtt92G8847D/fddx+GhoZ0ii4hn8/j\njjvuQD6fx8MPP4xo1PomJyAw21AVWUG4ZgvayZkIexKoBBFZeuDPd2sV1Vpn/KBvaAjYWouJUFWq\nukyRdX4vKZdlfPKTv1YmZxT4UQ+kUnk0NgZd9cWdmSkoE17VWumcfFb2KuUNFJorPPbYHuXfTlXm\no0eZJZQsooC9tZg38IWsxXZElFrvVBNZ/rrH6ekc9u4dw5Yti3X1pTz1bNTn085abFYjW0lAw2E/\nYrGAY0X22DFGiN0kFhO+9rV34D//8wOW6cuA9fmvBhvxE9lyWcbvfteLK69cgmuuWcE9XjVMqHD6\n/63vf2aY6/Y7vPukcrGD15JsBKoz3raNf39qob0vua+RVe24dD47UZcrF/no+NB92OlYaEHKaX2s\n+hnGwXx9fW6sxebnGheR3bFjB7Zu3YonnnhCZ3Pas2cP1q9fr1NOL7vsMvT09Cjvb9myRXkvFAph\nw4YNyvuEcrmMz3/+8xgcHMSjjz6Klhb3NxYBAbcQ1uLZh+gjK2CFygd+LBZELleat3NFVWSNJz48\nCbnJJEv9pPAegtuwpwcf/BNeeeWUQh6dKk2VqGVhjiU6BzRBQPzHhRFZ1VoMuOslqxIfNqGzUzDn\nEtPTOfzsZweV/3b6e44dq273ota11UZkKRDG7pwjhX/xYj35ovY7PNfezp2DKJdlXdAToLVGml8z\nIyPs++2txeaKbCTi15HHjo6I4wWf2VBk161rU/qvWsEq0IuS/XmsxQBw/vmtyr/vuutq2wRaLTwe\nSecyIUJr1+amEk7Ve6fgVanVHqPF09vxB2dV4p3vXIXBwc/hmmtWOt4W0Ad6TUy47SOrWouJyNpd\nJ1qoRDZ3+nPYfnRrLc7nS8jnS4oDxWm6d0OD3/BecPDgOAA+YjxrYU8f+chHcNdddyEY1J9UY2Nj\n6Ozs1L3W1taG4eFhAMDo6GjV++3t7cr7hK985St4/vnn8bWvfQ1dXV0QEKgH6AEuiOzsQfvgdtKf\nUODcANXIEpF1o9w9+uhuvOtdP7btXWkEVU01ftBHo36lXsn8M3KGk1A3YU8TExl85SsvoK0tjH/8\nR2YZrEWR/dd/fQ0dHd9UJiJOIMuyxlrMHyRE2zJrMdvOLpnXCmRFJQWvntbin/3sIDKZoqIkOP09\nRGS1ZIRIPl0LlYjHqe6P11psfYxIkdW23gGqbZpWeO01Vj6mDXoC+FpW8fSQZeOpVv1kWcbx4wms\nXt2sI3AdHRGMjs44qidXiezcCycsbMd4v1BwE29/TkouvuiiTrz3vee7GktljaxTouO0VZNT8FqL\nK+snrTIP7CBJktJmzA3UlF73imwg4IXf70E6nXfcegdgCwxer6SxFrNrx621GGC/59Qp/ppWLVid\nrf4cKZdl7Nw5iPPPb0Frq335qN/P9snMTI3WYjNkMhkEApV2qgDyefaF2WzW8n0A+OlPf4rnnnsO\n3d3dePDBB2sZjoBATRB9ZGcf2smZUGQFKkG1RCqRdd6C5yc/2Y+XXurHs8+ecPz9dooET9CEWfse\nN2FPu3YNY2amgI9/fLPS948USafYt28MX/vaC8hkispk2Qmy2SJKJRmxWMBRj1KAqYLlsqxRZNnx\ndUNkBwdZoNKSJYx41dNa/NhjeyBJwCc/eSkAveOEB0ePViuyLS0hNDUFcfjwuOE2lFpsN6EllYyX\nyFYm7ZIiy6Oybd9enVgMaFuQWFmL9QsTZjDqIzs+nsH0dK5KRe3oaECxWHZ0PPr7p9HWFnZFepxC\nkiTEYkHDRPb9++MIh33cts1rr12B97znPHzrW+92pMYStOSC7mtO98Hc18jyhj3pCbVTm/ZsQl3E\nKVS12HICOj6814kWdJ5Vhj3RfnIC7e8hIsubWKz9jFQqr1tgOngwjqmpXNW9wwqRiL92a7EZgsGg\njpQCrNaVrMZ27wOMDP/whz/EN77xDezduxePPPJILUMSEHAN0Ud29qGvkRX7VUAPI2sx4IzwUPjL\n008ftfnLahBhNpvIWfXCUz8jb2hNDgZ9KJVkRwFH+/aNAQAuvrhTqQl10x+zVCrjk5/8teIusavz\nNYK2l6OTHqXa76uskXVjLVaJbFQZDzD/iuzgYBKvvHIK11yzUllkcKPIBgJeLFumqqGSJGHTpi4c\nOTJhSM4nJviCY3jDnoaGSJE1DnviUdl27RrG6tXNVeRaq+KYYWSEnc92iqxRHWZl6x0C2S+dLPok\nk3lXoUBu0dhYHWRXKpVx6NA41q9v52750tQUwi9+cUtNtZyqIkthT25Ti8+MGlm63zgNzppNVNbI\nOlVj1c9htaluFFlAH5hI17LT48u2UX/PqVPsHqy9b/EgEvGjVJJ1zw1yc1x5JT+RNbMoE5xXAGvQ\n1dWFQ4cO6V6Lx+Po6OhQ3o/H41Xvd3d3K//90Y9+FBs3bgQAfPzjH8f999+P6667DqtXr+YaQ6Xi\nK1ANsY/4QBebLEtin3GAZx8lElm0tIQwOZlFoSCf0/v1XP7tZqAHbmdnDIFAAM3NbJFzejrHtb/y\n+ZISHPH008fg8/kd9QDMZhnRa2mJGH4ftZ3J543P3XJZRjpdQGNjsOp9Cs+RZS8CAb6JxIEDTJXb\nvHmJooJOTKjOJt5z6Jvf3I6dO4eUtjdurr1cjhGe5uYQGhrYxLBU4rs3lkq0QMD2S2srm4zNzJQc\nj2N4OH1atWTEhz4rk7H/rNm85vbuZcfmXe9ag/Z2RgJTqaKj7zh2bBLnndeCcFg/yb300sV48cU+\nHDw4iauvXq57L5UqwOuV0NzcYKnARaPs2ikWrY/16CizKq9YwSyq9Ld+P7O/5vNly+2np3OIxzO4\n/PKlVX/X2sqO0cyM+X4hq/SyZS2W39PUxI5zLqce574+dk52d7frtl20iE2wE4k89/FIJvPo6oo6\nPkfcnlOxWBCjo2nd9ocPjyOXK2Hjxq55ez7EYixB3O/3I5dj97/mZuP7n/lnsHPN6r5Sy+9JpxlB\nbmuzPj50jmSz7Jwl1a61tWHen7ctLezcz2RKmJzMoru7zdEY6G8bGgKYnMxifJwtYC1d2uTw2AQx\nPJw67X5lSmhjY8jx/iAXTT4vY2CAXXdr1rQjEOCnjerzU1XXd+4cAgBs27aKe0wNDdbdDGoisps2\nbcJDDz2EbDaLUIgN+I033sDmzZuV9//0pz8pf5/JZLB//35dCx6vV028+/SnP41nnnkGX/7yl/H4\n449z2SYqFV8BPSqt3ALmIMUwkymIfWYDnvOqWCwjnS5g2bJGTE5mkcnkz9n9Kq5DY5BtsqHBi3w+\nr9RUTk/nuPbXkSMTSv3q8HAKr712Elu2LOH+/kSCTaqDQY/h95HdcnIyjXy+OkmflIOGBn/V9n4/\ne36lUhn4fHy1e2+9NQK/34NVq2KKCjw8nEQ+n+c+h8plGffcw+ps77rravzDPzyHqamM4/NvYoJN\nXiIRH+gxnU7zHZdEgtniQiF2XBsaiJSnHY9jYCCJJUuiynahEDOSTU7OWH7WbF9zb7zBlIQLL2xH\nJMLGMDFhPQYtxsdnMDmZxdVXL6va5qKL2OL/zp2nsGWLPidkaiqLxsYgCgVrpVSSVPXdakwDA1OQ\nJKC5mS2uaP82FPLZPv8OHhwBAKxc2WhwzrP/t7p+h4eT8HolRKNey+/xetk1k06rz40jR5gwsmJF\nTLct9XMdHJzivkZYSUD1dWuFWs6pWCyAY8cmddv39AwCANata52350MkwpwiyWQGU1Ps/hcISI6+\n33eaOaRSWcPtar326L4cClmPi+6rdF/i3W4uEAiw+/3YWArpdAFNTdWOVPNt1f0VifjR3z+thKI1\nNTk7R6NRP5JJtj+mp7PK2Jzuj3CYnn0z6OtLoKMjAo+n7Ohz6Hk+MZFGLMb+/eqr/YhGA+jubub+\nrHDYp5REGKEma/EVV1yBpUuX4q677sLRo0fx/e9/H7t378Ytt9wCALjpppuwZ88ePPTQQzh27Bju\nvvtuLFmyBFu3bjX8vEAggHvvvRe7d+/Gww8/XMvQBAQcgyxZxaKwwM4GKI2RLGRzlXAosHChWouZ\n4kc1sryWTbIaXnwxCxX8zW+c2YvtwkHsrMVWtVxqXSnf/aRclrF/fxzr1rXB7/eiqSkIv9/j2Frc\n3z+Nqakc3vWu1VixgtU01WIt1rff4buGyXJI9lC3ScMzMwUkElmdDbZeYU+7dzMCt2lTl6uaX6PE\nYsLmzV2679AimcxxWSV5e/0ODaXQ2dkAn696+hcO+2ztopV9XLUIBLwIBLw21uI0uroabJ0Tqn1V\nPXfNAprIWsx7rdD14MZy6RbRKEuB1V5D+/ezUoILL2yf13EA7N7nNrV4vmpk7cbl93vg9UrKOOpZ\nI0v3JbLhOk0sJkSjfszMqDWyTq3F0WgAhUIZuVxROb5uamTJWpxMMmux0/pYoNr6PTmZwcGD49iy\nZbGj+mFmLa4xtVgLrUrq8Xjw4IMPYmJiAjfddBN+9atf4cEHH8SSJWxFfOnSpXjggQfw85//HDff\nfDMmJyd1gU5Giuvll1+OD37wg3jggQfQ29vrdHgCAq5QLstKPZkIe5odUD0chRWIGlmBSiQSWfj9\nHuWB5zS1mCbVt99+Kfx+D37zm2OOvt8uVMQuhdVqwuWU/J04kUA6XVDqLyVJQmdng2Mie+gQs8Cu\nW9fGFVZlBm39sNPUYvq+yuPqtEaW6jmXLlVrs+za1cwVdu8eQXt7GEuWRF21EzJKLCasW9eGYNCL\nnp5qIjs9nedqzcJzjGRZxvBwuqo+lhAK+WwXHK2ILKAGvJhhZCTNFWBDZEk7nt7eBDweqaoNCE32\nea8V9bqfzxrZ6nvb/v1MYd6woWPexqG9p9n10TaDFZEdHEwqi9huYdbSrBKSJJ0OAioo2wHze1wJ\nwaAXXq+kBCO1tNgn8hqB7pknT06htTVkuOBkBe1CHy0C1ZJa3Nc3hWy26Lg+VvsZdJ7t2MEcCE6C\nngA2fqvOAY6txQcOHND99/Lly/GjH/3I9O+3bduGp59+2vC9xx57zPD1e+65B/fcc4/ToQkIuIa2\nGF2035kd0CSvo4NNMtz01BQ481EolDAwkORuH6HF1FQOTU1BZVGTHny8SheRg82bu7Bt2wo8//wJ\nDAwkdcTHCqQcmU3ktH39jJBKmbeJcEr+9u1jk9qNG9VJbXt7RPmNvJg9IquSfKfqMk1wyVpGwSdO\nU34p6EnbKob29XymFieTOfT2JnDddasgSRKi0QAkCY4m7EaJxQS/34uNGzvw1ltjyOdLyv6mFkhN\nTfaKHU/7nWQyj5mZAhYtqoXI0u8wvt6NWm4QUin2/TxE1uv1IBDw6s7d3t5JLF/eqOwfAn0eb89l\n3vYuswltCyoi3vv3xxGNBhTnxHxAe0+zu/+ZwayPbKFQwhVXPIJLL12MX/7yFtdjnJjIcCuaxkR2\n/vMo6L4wPu6uhyyBnoGnTiWVVktOoCWy6oKi8ypSWvA4eJA9l9wosnRe0b369dedBz0B9kS8Jmux\ngMDZAq1qIojs7ICILK0qCmvx2Ynvfe9NrF//PceEC6DWNerquVMLqmo1bFZ6KjpJL7ZbwVebsRuT\nJkr9NGu/A/Av4JDNUKvOdHREkErlHfVr1BJZnmbyZqB9o7UW29lWCZVtH6hfIE3yeGGUsKsSgvkj\nsm+9xY7Npk3Mwu7xSGhsDLpSZI2ILPvsRcjnSzhwQA3IpB7GPAoTjwPALLGY4ESRNVu4skoY5e0h\nS9BanTOZAgYHU4ZtaojI0ufboZZ+o25RuQBTKJRw+PA41q9vc9VGxy2MrMVEbnlB13XluXL48ATG\nxmbw7LO9mJx0dq1rMTY2o6S284yFzpF6LFBooT2f3BJZumeXy7IiAjgB3Sto0Qhwq8iyz6HniTsi\nq18IVhOL+XMs2OcIIisgYAvtKrZovzM7ILWiqSmEYNArrMVnKQ4ciKNcltHfP+142+npvFIXC7ip\nkU2guTmE1tYwtm1jaa9vvTXK/f3qhNb4QalO+qxrZK2sxbxEdu9eNm6tIuu09g9gSaiSBJx/fosy\n4bTqwWcGOgZurMVEvMmC2N7OiCy1lODF4CAjXtRDFtBP1OYL2vpYQlNT0HGNrN/vMVXfqE5Way9W\na/5mx1ps1kOWwFMje/x4AosXR00nx6y9i/H1QkTaTBG2Gs+JEyyd3MjS3NERgc/nURR8O9D45leR\npUU6dt4ePTqJQqE8r7ZiwNhabHb/M4OZtZiuk1JJxnPPnXA1vnSaETBeEheJ+HTtd3gsyXMFLeFy\n235Hu6jgjshqrcXkjHHfR/bgQSKybqzF6kJwuSxj584hdHe3oq3N2e+KRKyvU0FkBQSgf/iLGtnZ\nAakVTU1BhEI+7lpBAT48+uhuPPxwT72HgclJtmDh1L4qyyw5VDuZdNJHtlyW0ds7iTVrmiFJkhLA\n40R9TKXykCTzB32lNap6e3MFQLXj8p33+/aRzVCt/3Na+wewFfSVK5sRDvuV8TtRdAn0m/VhT06t\nxez7/X4vmptDSusVXlT2kAUAn8+DUMg3rzWye/YYEdmQI0X26NEJrF7dbDrJVonssPKatpevHXw+\nDzweiUuRtbMWy7JxPVqhUEJ//xTWrDEvI4hGA8hmi4bPUaof5J0Uh8N+RfUjRdvouz0eCUuWRJWF\nDzvQueNUiawFtBhB323kwJgPqP1BC64VO6MgLkAfVvbb3zrLKyDQvY6XxGkXO6an+YLR5gra50Ct\niizgPOhJO4ZkMlejIss+h1wOtViL0+kCjh9PYHo6h8suW+z6c8wgiKyAAIS1eC6gJtIyRVZYi2cX\n99zzIu6996V6DwMTE4ycOCVLMzPMNqlVM52EPQ0OJpHLlRSFpjJYggepVOF0vaOxtU+1RlmHPRnX\nyPKTv3y+hEOHxnHhhe26NFentX+JRBbDw2msW8dqq2gCU4u1OBYLKDVxvKTcaALV3h52rMgODVGN\nrJ54NTZa9xWcbezZM4pAwIvubrVmjRRZqxASwsREBhMTWVNbMQBcdFEnPB5JRwaI9PBOzu2cL3bW\n4mDQh3JZNl3MPXlyCqWSbGjvJVjZ8SnRlXdSHA6ratvx4xQyZfzdS5bEMDSUQqlk//yme8R8KrJ0\nf6LzVg16mr/EYu040mmmyIbDPscKppm1eM8e5ippbQ3jmWeOcV0blXBKZKlGlhZG59MuXonZsBZr\nP4McOW62r91arN+PtYY90fV7/vnm90AziBpZAQEOaMmrUGRnB9oo/GDQJ8KeZhGyLGN8POMqxGe2\nQXWPTlsxaNu7ELSBKHagWj0iB3YJw0aoVIQroVqj3FiL+e24R45MoFgsK4nFBJrM8RLAw4fV+lig\nuv2BE2jrzZyGPZGVmQgwwH5LPD7jaHI7NJSCJFUriNHo/BHZYrGMvXvHcOGFHfD71ZChxsYgZJnv\nfLNKLCZEIn50d7di9+5RZR85sRYDOO18sbcWmxFZu/PFLrEYsLbjO1dkVbWN1HkzErxkSQzFYhmj\no/bXSj1CgSoX6epHZNX7ZDpdcEX8PB4JgYBXd8+XZRl79oxg9epm3HDDOoyOzmDXrmGLTzGGc0VW\nJdVMka0fkdUq/G6JrH7xrzZrMT233BBZrQoqSeAOUNR/hloja9Y6y+lYjCCIrIAAKhVZQbhmA4kE\nm4Q1NweFIjvLyGSKyOVKrpS22QYpsk7JklEwRzDohd/v4SKylVbDUMgHj0dybC22shdqbXhm2wPG\n1k8n5G/fPmObIU1keCbngD7oCVAtgLWlFgcd1/satX1oa4ugVJIdJRcPDCTR1VXd8zQWC84bkT1y\nZALZbFEJeiJQCx6e32MX9ETYvLkLqVQeJ04wwqgN3OIBU2TN77NqjawZkWXni9k1ZKeKAtprxkiR\nJSLLq8j6FauznS2aJto8dbJWIW1zhcpE9v37x9DYGHRFEGqB9p6WThdckRygup56aCiFeDyDiy/u\nVIL33NiLVSLLF/ZE45+eZjWh9Wi9Q9DWGrutkdUrsnz7QAu1PRkLCfR6Jfj9zqmedhyLFkV1i3i8\nqLQWA7B0c5hBKLICAhzQhz0JRXY2UKnIirCn2QOpoMViue4LL6q12NlChVFyqCRRGqw9Oahc4aX2\nB7yKrCzLmJjIoLnZfOKjteEZgcdazHN8iMhqg54A52FPhw5NAFCJbGWfRSeg6zcW04Y98R1jo5AR\nUlh4fwuRF23QEyEWY8fZjXXRKcjqe/HFXbrXnSRsU52ZttbXCEuXMoJHVnKqweUnstbOl+Fh9rlm\nZNCu3RSPqmLljBgYSCIU8qGtja/HpraXrEpkjSf3dJ4MDPAQ2flPt6VjmErlUSiUcPTo5LwnFgP6\ne1o67d6Ky4isep5oA9H+/M/Pg9cr1URkKSDODrT4QtdMvRKLATXpF6hfjazeWlxEJOJ3dY5pzws3\ntuLKsdRCZIUiKyDAARH2NPvQ1siGQiK1eDahbW1QT1U2kyno2mM4gRpko594NDUFXatc0aifm8hO\nT+dQKJQtJwt27Wus2ng4CXs6edI4kZVUibExvhrZSkUWYL/BDZFNpfLweiWEw5KT8PEAACAASURB\nVD7HYU9G/QtpP/O24JmayiGTKRraYOmcMVtgmE1QCvbFFxsrsjyBT7QwY6fSVLYp0i4G8iAQ+P/Z\n+/I4Oc7yzKf6vqbnnpE0Gt2H5Uu2fMiy1xg7YENsYxOSQMAcy5GFYDu7CeFIsDm8G/hhjmQDDke4\nQpZsOIKBbMABjJGxLNmWIx+6jxlpZjT3TPdMd0/ftX+U3qrq6jq+r7qne0b6nn8sT3dXV1dVV7/P\n9zzv8zr3yHZ2hqvmsBJYrcV2xaje2mjE8PAc+vpamAtrLXW7gLGxFDo6QuqiihF9fcp1wqLI2rUE\nLBb0bRMDAwkUi+WK72mjUJlaXOCeIUsIhfwVLiv6nlx2WQ9aW0O44YZ+PPvsWa6gOkC71/Fai8lt\n0MywJzq2kgQ1fJAXeoeQmx5Z4xxZt4p7IOBVlVw3QU+APqMhj1OnEohE/Myjt/R47Ws34V3vusLy\ncUFkBQQgwp4WA/rUYlIKGqGgXAjQk4Fm9snq94NXkbWa4drREVaTkO1w6lQCoZCvguhEowFmYs/S\ni+UUIFWvsCdS7IxqE78iO4329lDFZ3KryI6OptHdHYEkSa6JbChUrS6wfhYiJCtXmiuyQGNmydKC\nyZYtlaSDClUWIqu1WdgXt6RUksuBdy4mS9iT3egbp4WbgYEEYrGA7XdGOy6V3+FcroiJiQz6+9mL\nYv2Yl7GxtO2+8yiyzSSyqVQex44pzonNm617phcLRJQSiSzy+ZLr5OZQqLJH9oUXFCJLyd47d/ZB\nlrW+fVZQHgD7HFnlGqF7aHMVWeW9W1tDFaF9PKhnavHCgnsiC2ifxy2R1ffLDwwksH59myt1eOXK\nGB555LWWjwsiKyAAMUd2MZBM5hCJ+OH3e1UFQAQ+1Qd6otdMRZYKboCfUFvZ+9raQshmi7YKrywr\no3fWr2+rKBh4QoCoYLIrFmhV2kr5s1NkecKexsfT59K9K9WmaDSAcNjHRP4KhRJOnpzF1q2VdkV9\n8isr8vkSzpxJqmo37Rdrnzs9T6/IdnfzzZKlUSqktOlBFr5G9MkODiYRDvuq1BFSZFmsxdpMbXu1\niBRZGlNkFohmB7se2UymgLm5nKU1F7BPHKbvHI27soKVUk0Ek6cnlEhKIpFFIpG1JbI8PbL0+Zo1\nR/b4cYXIGhdHGgHjWBW3imw47DcQ2XG0tYXUOcnUssH7HXWTWgxon6eZiiwtCri1FQOVqi6rBb/y\n9ZWpxc0lssp7nzmTxNxczra3vhYIIisggEqCJRTZ+iCZzKpFDaWXisCn+qBSCW0mkdUItdvUYmMx\n2dGhFAF2quzsbBbJZPUPYyymqI8sIzgmJ5Vj6LTqbafy0hxas2JQUzGdj8v4eMrSctXTE2UisqdO\nmdsVo1E/92LHwEAC5bKspuzSjFLWhSizsQ+dnXwJzCyKLC2GOCGXK+Kzn93LPMZIj8HBBNauba0i\nbzzWYrqWnQpc6gukBSJea7FdFgH1mJr1HBPsrMUTExmk0wXH1FGrECze0TuAZhslS7Ndj7GmyDrP\nkm1OarGmlJEiqx/n1CgQuaDvgj6giAfUIyvLMtLpPE6cmMHll/eo3xMtdIhv3vPkZAbhsI+ZYNM1\nuzR6ZJV9qYXIEhnu7Axzj0UCqlOL9YuJ/PuifJ7+fnc9svT9pQwIN/2xLBBEVkAAlcWm6JGtD5LJ\nnFrU8FoTjdi9+wze9rYfCyJ8DkulR1avyPISaitrcXu7UszbEVki8sZURyoCWBRIzcJmT2SVvlvr\n8TtWc2g1F4L9/aRQKGFqasFSKevujmByMgNZtrflU3+sUeWJRBTlhMfWb9Z/7JSIqwcdf1LUAP6w\nJ7uZpxqRZbvm/vmfD+Kv/urX+MY3DjA9n5BMZjE7m8W6ddUFGJFLlmCyZDIHSTJPt9bD2CPLO0c2\nEPBazoF1SiwG7MOeKOjJqRgla7FRqR4ZURKL3SiyRGTt9p1CpNhSi5V7j1tbrRtEIn54PBLm5/M4\nfnwakuRuFEmtoM9MSeiRiFtrsQ+yrIgAL788CVlW+mMJRNx57f9TUxm1pYEFRJY0a3HzFFm6L7lN\nLAY08shqra7eB80pksuVmqrIejwSolFNuV+s610QWQEBVBabgsjWDlmWK4gsKbKshbAeyWQWb3vb\nj/Ev/3LI1Vy68xFLsUeWZoeywspaTKvZepJsBJEHY4HPQ3BYrMUAKbJWPbI5SwVAI7L2x4UKSitF\ntqsrgmy26BhiZZwhS6BChmehwZzIsieP04ITFZmAPuyJT5E1Iz5mI15+9rMTeN/7fmaqxlN66unT\nc0zvTaAQrnXrWqse41FkE4ksU9+csUdWSy1m75EFzO+zGpFlsRZbE1kne6DVcdEUWX4iOzCgvLcd\nkQUUVZa1RzYc9lWNdVpMUKq6QmRnsHZtW8Wc5UYhEFBafeh6qEWRBRQnjlkgmp5QsUKWZUxMZJht\nxYDWvkCJ3M2cI0vErx6KLM8x0MPn8yAU8qnEXn8P5kWtRBaodCsJRVZAYBEh5sjWF5lMAcViWV2d\npwLLjaL60Y8+oaozS2Fu6lLAUuyRda/IGomssyJLRbJxdI5TOJMerL1YsZi1NTeVsh5fwdojS5a4\n3l7zIp16M51ssVpicaVdUbOLsn/3TpxQrI+bNhkVWXZrMc31JfAqstQja6bIUvGtX2D49rdfxDe+\ncQDHj89WPLdQKOGXvxwAoM0xZcXAgEJk166tLsD4Uotzjv2xgJkim4fHIzGrKna9zHbHk6CljFZf\n79TX6RRQpCnVRiKrHHu+sCdlf2h0h92+A8qiRyqVdyRPqVS+KRbUeDyAkZE5jI2lsXlz49VYQizm\nV1uoaumRBRQiOziofE/01wZdBzzW4nS6gGy2yBVyZLQW60fgNBpEQmtRZDs6Qvid31mHO+/c7Hob\nLS0B9XjUosj+7u9uwq23brBd/HKC3vUgemQFBBYRYo5sfWEkGm7DnvbuHcbXvvaf6v8LIqtg6fTI\n1j+1mFazWYisUZG1C6sxgkeRteq7nZ+3LohZ7fSkjFgpstoIHnsCePToNPx+T9Wqt9NIFTOQIqu3\nggWDXo4e2WKFrZj2IxLxq0FGThgdnUcg4DUNPKHzrFfeafFiaChZ8dw9e4bVa41FrdNjcJDGzZgp\nsuYWWjMkElmm4tbn86C1NVgxficeDzLbLO2uOTZrMV0r1d8fWiBwIrL0Oa0U2cWyFgPsycV2C1CL\niVgsoOYKNCPoSb8fZv/mgabIFkxt4zwLPQQiX3yKLFmLafxO8xRZ+vy1KI9erwf//u9/hPvvv9b1\nNvTXWS09sn/6p9fipz99o6teXf2+AEp41dq11ffRekAQWQEBCCJbb9CPF5EUt2FP9933GGQZuOee\nSwGYF1gXIurdIzs5mcFb3/oot3V7cRRZdiJrJAf6uH8nsCqyWnFfuc1isYyFhaJlTxZr2BNZwOys\nxYC9IivLMo4encHGje3w+ytnhLolsj09kYqFAmVGKXuPrJkS0NUVZg57Gh6ex8qVMVMSR+dZv2BB\nRPbMmUrVlWzFksSvyBKRNeuRZS3Ui8Uy5ufzVe4BK3R2hnXjd/JchbnWwmFNZO1UTTtr8fHjM4hE\n/I5E1NpaPIdw2Keqziwg1Y8s3s6KLNssWWUBqvHKnf771IygJ4JeJXOr2Ol/02nhQB8kZjdP2Aq8\no3f0+0GLP81MLd62rQt79rwD73//1U3bB6DyN7UWRbYe0AKj4pYzoGuFILICAqjsYxM9srWDikq6\nofKMIiHMzeXw4osTuOWWdXjVq9YDEIosod49sp/85G5873uH8aMfHXW1Hx6PxN0jazUjkwpdPVk3\nwkqR5bEWT01lEAo5p2NakWO70TsAEAgo17xT2JMTkWWxFk9MZJBIZKv6YwF+IlsolHD6dFJNLCbw\n9sgaFVlAIeUswVWjoymMjqZwySXdpo+bhRJRwWxUZB977BRCIR9uvHENkskcl9WRLJNmPbLRqB9e\nr+SoyNLjpOA6obMzjOnpBciyrCqyrCDni9l5sgvPIliFPZXLMo4fn8HmzR2O6nAg4EU47KsKwRoZ\nmcfq1XGuOZJ0DZETwInIsiiysiyfU2QbX+Dr7xXNmCGr7Ydf9+9aFdkihofn0dsbVa8/QLs38/TI\nUl6AG2sx3VKamVoMAFddtbLp5HEpEtnF6o8FBJEVEAAg5sjWG8YiPxTiTy2mnq5161p14S6CyAKV\namWtRPbYsWl8/etKmiuv9XtmZgFer4Tu7ogrRZaCKfTgCXsy9h3yWotZ0jGttuk0wiMQUH5ejWFP\nyWQW3//+IZXMEZG1sk1qvaUakT12bBrr1v0ddu8+A0DfH1tNZO1UNjMMDiZRKskVQU8Af4+suSKr\nBFc5XbP79o0AAK67rs/0cSrE9QsWdG8YGtJU1zNnkjh4cBKvfOValZiTxZUFp08nEY8HTW3BkiQh\nHg9WjZkxgnX0DqGjI4x8vqT2evIQWXIBmAWMjY2l0doatA1/sVr0OHt2HplMgZl8tbYGKxTZbLaI\nyckMl60YqEy9jseDjkU5yyzZdLoAWXZP4GpBpSLbPGuxXpGttUc2kylgZGS+6tzSfZGHyJIia5zZ\nbAejdbaZqcVLBfpru9lElvZFEFkBgUWGsBbXF1Q00w8mqVM81mIqRlatarHt3brQIMtyXRXZBx/8\nDUolhVTxpkpPTy+goyOsjnjhAfWXGokkFfx2BKFe1mKWlX8rlcpJkbVyIfz93+/HPff8GI8/PghA\nI7LGUUIEM6v188+PYXQ0hX/+55cB2BNZXkXWLLFY+Tx81mIzRZY18GnvXiciW628Uwq2nsg+9tgp\nAMBtt21Q03JZ7cWyLGNwMIF166pnyBLi8aBjoW616GIF6gkeHp5HqSRzKUx2zpexsZRjj6nVoocW\n9MQWUGQ8Lm6CnoDKxFWWwBkiU3aKrOYWajzhoXPJYtFeTOjJq9sRRPT9HhmZRzZbrEq2DQZ9CAa9\nXNZiui+4UWQJzeyRXSrQX9vNJrJ0rS3mqClBZAUEIObI1hukXpFyoikFPIpsNZEV1mKlECsWy2ph\nV8sx2bdvBD/60VGsWqUUuE42WCNmZ7Po6AgjHPZxK7JWyaGaImtNZKlIdmstTqfzWFgooqvLuV/P\napvORNbchUD9fi+9pAyJHx9PQZKse3U1Iq29P53zJ58cArA4RFafWKx8Hh9KJdk09EqPYrGMQqFs\nqvxRgerUJ7t37zC8XglXX73S9HGzxQVavND3yP7HfyhE9jWv2agW2qyK7NTUAtLpgml/LKGtLeTY\nI5tImC+6WIGs9TTuxo0ia7zmcrkipqcXHK25Vu4DIrKsKmJbWwiJRFZ1HRCxrEWRddp3QLMWk5vH\nDE5OisUEveemTe2Oo5gWE/UIeyInDSWcm51bloUePYjI8iiy+vsMT8L3+Qz9tW22oNhI0L1aKLIC\nAosM/Q+/ILK1g4pKzVpciyIbs1TFLkQQwaPCnLc3VY/PfOZpAMAnP3kTAOeZp3rIsoyZmQV0dIQQ\nifi592NuLmdaRIXDfoRCPltFlh4zBuiwWotZg54A/aiXymvPqSC2CnuiXsUjR6YAKJbP7u6I5UxL\nKkT0x5dI6fHjMxgdTalE1ixAhpfIUmFqpsgqn8d+MYoWNKzCngB7RTafL+H558dw2WU9lmqRcXGh\nWCyr95bh4TmUywqBeuGFcaxYEcWGDe2qIksJq06goCe7pM3W1iDm5/O25J5XkSWyT/25PMqh1TVH\nMzadVE2ra+XYMbbRO4R4PIhCofKcAPzzKPVFuJOaDCiLYOGwj0mRdWuprQV0LptpKwbqZS1Wzg0t\nfJnNB25pCbgKe3KryJo5fC5ELCVr8Zo1cUgScNllPc5PdglBZAUEoPXFRqN+0SNbBxiLBdYiWA9a\nVV+1qsVV8ur5Cuod1YisO7t1LlfE448PYuvWTtx88zoAfIp5MplDqSSrimw2W1QJhBNkWT6XyGpe\npHd0hG17ZJ0VWfvrhKdg0mZrmvfIOlmLjceUrMSHDytEdmIibTlDFjBXZPXfg6eeGsLRo9NYuTJm\nGihEPWS1W4vJtmq/YEEk1Syll0WRPXBgDLlcydJWDFT3yOoDnAqFMsbH05iby+H06aQaGNXXx6fI\nknJuFvRE0GZlWn8H3SqyRKRZCTCgtXAY77NaYrG9Iur1Kj3rVtZio0pvBWNyMRFLM7JjB73axqLI\nSpKEVatabHtkm6nIku21mTNkgcqwp1p7ZE+cUO4X9VBk3Y3fqeyjFlhaYU/33nsNnnnmndi2rWvR\n3kMQWQEBaD/80WhA9MjWAVQ0UxHOOopEj3pZix966Em8//0/537dUoVGZJXCwa0i+9RTw8hkCrj1\n1g2uUqWpT7ezM6wWNayKey5XQrFYtiSB7e0h2/E7iYSi5hrn29H2nJJp+RRZK2uxeeoywe+nsCdz\nUnH48BQWFgpIJnOWicWAed+inpT+x3+cwpkzSVNbMQBEInxuhpMnZ9HdHakixayLUUeOWNucNSJr\nvUjh1B8LKA4Pj0dSP5Nx4WJoKImDBycAQCWy/f0t5x7jU2TtrMUsI3jIPcCTWqy8v0KkeXr+rFo4\nWGbIEqJRf9Wix/HjM+jujqC9nW10Dn1WIvF0zBdbkQUUQjUxkbZckNZaAhpPeoiMX355b8PfWw/9\nfbfWHlnNWlx9buNxxbHAusA5NbWASMTPtU/6xY5mJxYvFSwlRTYS8S/69S6IrIAAtOIsFhNEth7Q\nigXlJurWWhwIeNHVFeZKozXiy1/ej3/4h/9UlablDqMi6+aYAFr/4K23btAl7LITWRqPQ4oswK76\n0Sq9VeHR0RFGIpG1LICSyZyp4qfNF62fImu1TSdlR5Kkc7NXtWNaLsuqIptM5nDgwDgA69E7AEyP\nrX5ffvCDw5Bl67mUpFiw9DAXi2UMDiar1FiAn8hedFH1CjwtHNgpshqRXW35HEmSEIsF1PsM/Zf6\nDoeG5vDSSwqRvfRSxdIWjQbQ3h6ytZ3qMTCgEEm73i5SgIyjZvSgx1jnyJIiOzCgEOl6WIu10TvO\ngUnRqL/i+srnSxgcTHDNPSWCPzenfHbNXcNGRgm8iiy9hyxrdmojjKPhGok//MOL8eijf4C7797a\n8PfWQ08U3Y4hot90anUxU2S1WbJsquzkZJpLjQWUBUOvV6p4vwsdS0mRbQQEkRUQgPbDH4sFRI9s\nHWAMwqH5cjxEaXQ0hZUrY5AkSWfv5FNkp6YyqvrDOyN1qYIKh1WrYpAkcKcFE37xC5qv2V+TIkup\nxQD7vhAJtLMWy7I1QZiby5q+ljXsaXJS2XceazHvHFmgemTN1FRGTYgGgCeeOA3Anshqds/qsKe+\nvhb131aKLI+b4fTpJIrFsimR1Wyr9ueYen/NiCyLtXjfvhH09kZtLb2AUoAbrcVEOs+cmcPLL5Mi\nq+1HX18Lc2oxa48sYK/I8o7f0RRZ5f35wp7Mv8dEZFlUTaXfXbtWBgYSKJVkrrmnxuMyNpZCIOBV\nSTor3CiydI1NT5tfY820Fvv9Xrz2tZuaGvQEGK3FtSmyBCtrMQDMzTkvtsqyjMnJDDeR1dcHYvSO\nAkFkBQQuQGjWYj+KxbKatijgDlQ0002UV5EtlcoYHU2pK/herwfhsI9bfaQQHAB49NEjXK9dqiBF\nlgikG7v1yMg8Xn55Ejfe2I9w2K8uNPD0h+v3g1eRdSKBZGE0sxfLsoxkMmfaO2g2X9QMRKR4rMXW\nc2StiyfjyBqyeFLokUZkndNk9eeZ1NXbbtug/s0qQIanv5zIk5kKyZo8fuTIFHw+DzZurN6GU9jT\n0NAchofnsXNnH8N830CVtfjii7vU7bz88gQkCRW9WatXxzE/n7dVUAmnTyfR1RW2XaggC60dkaXH\neK3F2mIPv7XYeJ+lNg2W1GDjtcYb9ASYE1lalORBLUTW6hqj4+qWwJ0PoGs6GPRahsw5Qa+Wd3dH\nquaBA3yK7Px8HrlciSvoiUD3ONEjq0BvmzfO2T0fIYisgAC04oxuiEKVrQ1UCGk/mHyK38SEolzR\nOAWgusBiARFZr1fCs8+O4syZJNfrlyKIQHZ2hk372Vjwi19otmJAsWT6fJ6G9cg6W4urZ6cS0ukC\nSiXZlBiEw35IUr2txebkmEXZCQZ9FcSP7I433bQWAPD008MA2NJkzazFt922Uf2blSJL54blOiHb\nsxlpYLEWy7KMw4ensXlzB/x+b9XjbW0h+HweS0X2uefOAgB27rTujyXorcV0Li6+WOmHPXMmiZde\nmsD69W0VhIXs+E724nJZxpkzSdv+WEBvoXXukWW1FhORJfCoTEQmjOeIAq5YiCxda2Tr12bIuiOy\n5bKMsbE00xxYI/TkiNWWTMdPP2tbD1qQupBtqPS7XEtycyikfb+triu6R7MEPrkZvUOge5yYIaug\ncvyOUGQFBC4I5HJFBAJetfgSfbK1wai48aYWj45qQU+EaDTATdqIyL7pTZcAAB59dPnbi4nItrcr\nBNINkdX6YzUiFAh4OXtklQJdby1m3RcnNZMUWbPkYi08p/q1Ho+EaDTAYC1mV2StRj+xWIv9fq+B\nyCqK7CtesQYej6R+H3p6nIlspbVY+fcrX7kWfr/iVujvNw/SoWKVh8ia7Q8tRtktVoyOpjA3l8NF\nF5mTakmS0NUVtgx7ol7KDRucZw7GYsrCVrksq8dj7dpWRCJ+PP/8GKamMmrQE4EC0pwCnw4dmkQu\nV1IVXiuwWIuTyRw8Hol5Xmc47K9QIvlSi83vs8PDcxULTnbQ2gS0EU8AH5GNxynsKYupqQyKxbJj\nYrIZJElCOOxDJOJnJp70nXZSZC9kIkv3BLczZIFKgmRFZIlYsliLJyeVe487RVb5vtTyec4nCGux\ngMAFiFyuhGDQqyaNihE8tSGdzkOSNGsYr7V4ZITCSbRVeDc2Wgqe+Yu/2AVJOl+ILBHIkCtFtlgs\n4/HHB7FmTRxbt2rFqbGf0wlaj2xIPc+sPbJOgSvUS2c2S5ZW960KfH3vpBWmpjLw+z1MJKG7OwJJ\n0gp6ApsiW3lMiciuXdtaQdac1CrjeV5YKMLrldDaGsSHPnQ9/uzPdlr23WmLDNXnJpHIVrhPaPxF\nb291McmyGEUjhexGLXR1RSwVWeprNKqSZqAFhkymUHEu+vvjquJ66aXmRNZpBM9TTylK+a5d1oFT\ngL4H0F6RbWsLctlq9Z/fTdiTfh60LMsYGZlnTgw2LtycODEDSQI2bGAfGUPq89xcTpeYzK/IAsox\n7utrYT5+nZ1sPbIXMumhz14LydGr5VZElq5dFmux5vBxo8gq+yKsxQoqU4uFtVhA4IKARmSVQkBY\ni2tDKlVANKoNJ+cNezLr6XJrLe7piWDbti7ccEM/9uwZVgur5YRstqiSupmZBXi9EuLxoCtyf/To\nNGZns7j55nUVxaGSsMseHFXZI+tWkbUnskTa9XDqOdT3TlphcjKDrq4IU3Hc0RHGzp19+O1vhyoI\nmJuwJ7IWr1gRqwhDsgt7AjS7J/Xup9MFRCJ+SJKEBx64EQ8++ArL11qFPU1NZbB+/Rfx8MNPq38j\nImuuyDqP0NISi80VWUAhsslkzvReQEotT5p0KpWvICd6ZbpakSVrsb0iS5ZvJyJLCyFmCy4EpZ+b\nrT+WoA9F4uuRrbYWJ5M5pNMFJlsxUH29jI6m0NMTNe2BtIJeqSaVnTV12IivfvV2fOlLr2F+vpaM\nba7609gsQWRrVWS168FqkYRHkaX7Omsomh4i7KkS+t/VC6EXXBBZAQEoBCsY9OkUWUFka0E6na9I\nRuSdI6ufIUuIRv3IZosoldjOTTZbxOBgQu0dvPPOLZBl4Ne/HmR6/VLCf/2vP8WOHf+AhYUCZmYW\n0NERhiRJ6jFhndMHQB1DZOypDAa9XNd9ZY8sn+Ku9chaWYs1a6IRdtZioLJ30gpEZFlx111bUS7L\n+Ld/O67+LZXKw+ORqtI79bAKe1qxIqaqln6/x3E+ZyTihyxrincmU2Dub/P7lUAX4/id4eE5ZDIF\nPPfcqPo3shabEWuWxSgWRZbOrZkdV39NOUHfu0zW4lgsgDVrrIkskTknRXbPnmF0dIQs+44JpAAR\nkTaDosjyFed6VYovtbhaNSd1mpXIGtsEpqcz3GnDWghWVr3m3ViLAeA1r9mo9pSzgK4dK9WfgsGE\ntbi2Hln9fc9JkWXpkeXtJa/cFyKyF+451YOOgyRp94TzGYLICghAKc4CAS3BTxDZ2pBOFypWAjVr\nMWuPbPXcQd4RPMePz5ybr6kUo2QrtOtnW6o4eHASIyPz+PGPj6lEFuAL8iHQfEpjH6Lfz6fIzs4u\nIBTyIRz2c/fIslqLzXpkWa3FVsnj2WwRqVSek8huAVBpTZ+fz6OlJWCr6lLfMe3L+HgaHo90ziWg\nXJc9PVHHcRzGPtdMpsBlCzRzM5DVmBaNACVkLRr1m67iswS2HT06DUmy76ckUme2SEHkg8danE5X\nW4sBZYHAuB+kHNn1yJ49O4/Tp5O47rrVjueFrl+rhZNCoYR0uuCCyCqfX5L4yIbZgiGNG6L7nxP0\nM7tLpTJmZ7Nq2jQr9IpsrdZiXtC9wyrsicVJcb6DCGYtah1bjyy7tVhboHSvyIqwJwV0Xsm1c75D\nEFkBASg//HprseiRrQ2pVL6iUOBVZElF0Cuy2hgUNrJEQU9kc6QVZDfjapoNInTf+taLmJnJqqm+\nPEE+BBqxYkxkDQb5wp6mp7Nqwc3bI+tkLbYbv5NI2BPZaDRQoV4aoQU9sRfnGze247LLevCrXw2q\nRRkRWTsEAj7IstaqMDqaQnd3BF6vR1UtnWzFQPUiDlmLWWFMPVa2oZwDfYLvxETaMniKzVo8hfXr\n22xDhai4NRuBMz29gNbWoGnisRF6azGRk2jUrxLZiy7qqtpOJOJHZ2fYNrWYbMXXX29vKwa069dK\ncaJrlVdlou93PM7XW6vN+nWvyOqtxYlEFrIMbkVWf1zIWqy/ly8mfD4PjnGsQAAAIABJREFUOjpC\nNmFPOYTDPtdjZ84HdHWF8Wd/thPvfe8O19sIBr2gS9Mp7Ill8djtdwXQ+kCFtVgBhctdCEFPgCCy\nAgIAqEdWsxaLHln3kGUZqVS+QkmwC3symw179uw82tpCFTdivVLAAiKyZA/Uh8MsJ8iyrBLZX/96\nEOWyrBaWvCo1oCmyxlmhwaCPK+ypFmWYyKCVKkLbNSOymiJrvnLvpJLxjN7R4+67tyCfL+HnPz95\nbvs5R1XHaPUcG0upo222bu1EW1vIMRkXqLZ78liLAWWhwXhuSJGdmEgjny+hXJYxMZG2JNZOYU/T\n0xlMTGQqen/NQIWqubU4w6TGApXWYk3hD6K/vxUAcNllvaavW7kypjo+zLBnD1t/rLIP9teaZpd0\np8jyJBYD5rN+SZHt62MLe4pEtAVD6jNlPScEr9eDeDyIRCJXYadvFDo7I7ZhTxe6BVWSJHzqU7dU\njO9ysw36XbfqkdXCnlh6ZN19VwD9+B1BZAnt7aELxnUgiKyAAKpTiwWRdY9stghZrrTEWfXX7ds3\ngo6Oz+GXvxyo+PvoaKpqbiCvfbWayPKrl0sByWQOpZKsXpsAqoissf/RDgMDCbS3h6oKhkDAw6zI\nZjIFzM3l1GAVTZHlC3uyKjxaW4OQJMW+bIQW9mTdI6t/DyN4Ru/ocdddWwEAjz56TN2+U0GsVzGV\nXs6CarGMRgPYv/9d+Ju/udXxvfXXbrFYRj5f4poPaDa6KpNRjo8sK9+3mZkFlEqyDZG1txZT0JNd\nfyyg75+sJLKyLGNqaoF5gcHKWnzttavw+tdvxXveY642dXVFMDdnHjYFKIqs3+/BVVetcNwHr9eD\naNRvGWZDxTkvIaXvN6/CZKaakyLLay3OZAoVgW68aG0NqqnFPp+HmwzXgu7uCKanF0yzA5RF1guj\nwF9shMM+dHSELJU/llRvgqbI8hNZumc1yr6+HPCFL9yKz3/+1c3ejYbg/M9lFhBwgCzL58KexBzZ\neoDCNPSrgZIknUvFrSweDx6cBAD89rdn8KpXrQegkKHZ2Sx27KgsJK3meVrhyJFphMM+rFmjKDRu\n1MulAOr1uuOOzXjssVPIZAquFdlyWcbgYNJUBQwGfSgUyiiXZcfeQOoxXLu28tjWy1rs9XrQ2hqy\nsBbbkwMn5d6tInvppd3YsKENP/vZCUxMpJHLlRCL2RMNvYpJCcx6ZYp1JIr+PNPn4lFkIxFrRRZQ\nHBB0PK2sxVYzSgkU9GSXWAxoiqyxRzaVyiOfL3EospoaSsckEvHD45Hwf//v7yEQCCCfr74GtFTb\nTJXdNZ3O48CBcVx99UrmhYJ4PGjZA+i2OKdrk7fnz0w1p2ArN9biWkaixONBlUSvXBlzvKfUE52d\nYZRKMhKJbBUJT6UKrmaVClTjVa9a79BGYG+918Ppvm6HP//z6/Da127Epk3ss47Pd9x55+Zm70LD\nIBRZgQsetDJP6Z6A6JGtBVZhGqGQr8paTM+lJF3AuqfLaoyIGcplGceOTWPLlk61gOJVdJcKSBVZ\nu7YVb3jDRQC0HjrqDWL9TGNjKWSzxSpbMQCuGcpEZKkfkRRZHmuxU5BNe7s5kXWyFmsEx3xfaARO\nby+f1VGSJLz73VcinS7gbW/7CQDnlEy9E6EWi6WenBMB5SOyARQK5Ypzqz9XIyPztonFgPmMUj1O\nnFC+w1u22BeT8TiFPVUWtzyjd4BKa/H8vNKTz0KWiCibhQE9++woSiWZyVas7UfAUv1vvCJr3iPb\n3h5iViH11xpPirQRbW1BJJNZjI6mGq6U0TVkTC6mtpcL3VpcL3znO3fjq1+93fLxUEjpRWazFucQ\njfqZ+uONiMUCuPbaPu7XCZwfEERW4IIH/ejrrcVCkXUPK8XIOFNTea5STJ86lVD/piUWVxJZHvVx\naGgOCwtFbN2qFdXL1VqsLybvvfdqrFoVw/XX9wOo7GdjgVV/LMCWSkuoJrL8imxLi32QTXt7yDS1\nmGX8DmDdt0iE0mhdZ8F//+/X4sYb+9URTixhT4ByTIkouinq9Yq3Xn1kf331QoORyNrNkAW0Pner\n62N8nG3MCimyc3OVixTU08hKmozWYlZyQoqsWRjQ/v3KKKLrrmMviltarImsW0WWjgFvz59ZFsHI\nyDyzGgtULvjRvceNtTgeD0KWlUWcRvbHAnoiW3n/yGQKKJflC6Z3sNmQJEm1mDthdpZ/TJWAACCI\nrICAjsj61NVA0SPrHlqCKLsie+qUpsiazZBVtsce9nT8+AwAbfQOsJytxUrB3dERxhVXrMDAwH14\nxSvWANAICmtvKiUWmxFZljmhhKGhJACNyPLuBwvxaG8PYWGhWHXNzM3l4PVKloqk03VSizLq9Xrw\nrW+9TlXEWcOe8vmSukDj5n21azevLhbwphYrrzcnsmfPsiuy1kTW/vUEbfxOrYpspbWYVaEmm6xZ\nGBA5AHjU+ng8qPYuW22Pt0Bft64N4bAPmze3c73O5/PA45HU7/DcXA5zczkuIqtfIKCFJDeKrH6h\naeXKxhJZq1mytOAgiGzj0NISZA57EkRWwA0EkRW44EE/+kKRrQ+oWCbrH4FmaupBRHZmJqsG+5w5\no6h91kTWmSyNjlLAidaDuFytxXb2Pt6+YVJkjaN3AGeiogedI/eKrHPir1VycTKZOxcGZa7mOimy\nRChZxt6YYfXqOL7yldshSebHUQ99+E59rMUFV4qsmRtBf80oiqxS9FuP3yFF1vwcT0xkEI8HVVXQ\nCtqMUXNFllX9M1qLWW24doqs03xj8/2wvt7cJrF2d0dw8OB78eEP38D1OoCcL8o50oKe2HqxAase\nWTdEVvvMjSay+j5oPdycX4HaEI8HHBXZcllGMplzNXpHQEAQWYELHvSjHwyKHtl6gHoTjYqsvsAi\n6Itpshe/9NIEACVcRw8e0mamDnk8kukYkqUOu+RQ3h5ZO2sxnyKrEFkqkHn3Y34+7xhkoyl31USW\n+izN4NQjS7Nc6fO6wetetwUnTrwf999/je3z9HbtWqzF2nijonr9843fqSayRkVWsxabK6JOYU/K\nDFpnNdVZkeWzFs/PK2nQxoUzK9j1yLoJ0rILtNHG7/AX6H19LY6LAmbQj9Gi0TusicVApXrPa/fW\nQ6/INtpabHWOrfIbBBYP8bhiLZbl6gRpQiqVR7ksW+YeCAjYQRBZgQse9KMfCPjOm9TiQqGEN7/5\nR/j2t19s+HtTMWge9mSuyAKavfjAgXHE40GsW9da8VxNVXK2KVGgj5E0RCL+ZWgttrZc8tqlBwYS\n8HgkrFlTrdA4ERU9zpxJYsWKqFpoE1EymxNsRD5fYkr8JeL+9NPDFTZhp5V7FmtxPQrr1avj6sKX\nFQIB5fFarcX6a5/Cnqg/mu/11UTW45EwPMwT9lR9fZRKZUxOZphU7lgsAEkyU2T5EnLp/kL7zUpO\n7BRZN9ZTe0XWPphsMaDPItCC8/gV2Uwmj5mZBUiSYvPnRTOtxXSvNJ5jp7R0gfqjpSUAWbZ2yAC1\nLfgICAgiK3DBw8xaXCotbyL7L/9yCD/84RF873uHGv7eWo+sWdiTeY8soCiy6XQex45NY/v2nirr\nKI+1mIJnjL1u0ah/GSqyyo+8uSLLN0d2YCCB/v64aTIkEVknN0K5LGN4eF61FQMKGQoGvUzHlkaV\nOBWT1Nf3vvf9DF1dn8eDD/4GxWIZqVTeNgXWjliQDbVRKapGRTYeD3JZggn6a58WckgFZ4Fmq9e+\nf3Su1q1rxehoCmNjKYTDPksSZ2ctppmd3d3Ox9XjkdDaGqqaI0s2UPbxO8pnIss2KznRemSrFVk3\n1lO7WZnNKNCDQa2FwyoB3g5Ga3FbWwheL3+p2ExrMRFZYx+0tlAhCFOjoH0/rIlsMxZ8BM4fCCIr\ncMFDby0+HxRZWZbxuc/tBdCcflDN+mi0FvtQKskVoSiV1uJZHDw4CVkGLr+8t2q7bqzFRqtjJLL8\niKx9jyw7uV9YKODs2ZSprRio7Oe0w/h4Gvl8Cf39lYp5OOxn6pGlYtLJWvzOd27H//k/d+P++69B\na2sQX/nK82q/rF2aK5EQM2uxVSL2YoEWB/7xH1/E8eMzrvty9f3dbqzFZso9/Xvz5g7k8yUcPTqN\nnp6oZe+xXQ81KV8s1mJAUeuMRNbOeWAG+ky8iixZl439k4BCZD0eicvSS9ebWaBNMpmDz+dxtXjh\nFsq8buV7yDtDFqi8z05PL7iyFQPNtRZr57hysULrWRZEtlGg3nW7PlmhyArUgroQ2bm5OXzgAx/A\nzp07cdNNN+Fzn/uc6oc/e/Ys3vnOd+LKK6/E7bffjt27d6uv++IXv4g3v/nNFdsql8v4kz/5E9xw\nww0YGBiox+4JCNhCP37nfOiR/fnPT+LQoSkA9naexYJmLa5WZIFKopRK5REO+yBJyizZAwfGAQBX\nXFFNZPXJrU4YG0ujqytcpTwuR2vxzMwCYrGAaU8nT4DV6dNKv9yGDeZEltVabEws1vaFrf+YtU/N\n7/fi939/Gx5++FV4wxu2IZHI4he/OAXAPjxHK8Srr5NaApfcgK75H/7wCDweCf/tv+1wtR39eaZj\n7Ca1WK/cLywUIEnAhg3t5/6/aEu06bOY2cdZE4sJbW2hqt7n6ekMl43V61UIIi+R9fu9aG0NWhDZ\nAlpaArZjoYygQt2MyCqKpv2YqXpD3yM7MsLfIxsIKL+DNH7HLZGlxSavV1Lt3I1CLBZAMOitOsdu\nxyEJuActWJITxwxaure7a03gwkZdiOzHP/5xTExM4Lvf/S4efvhh/OhHP8I3v/lNAMD73vc+dHR0\n4Ac/+AHuuusu3H///RgZGVFfa7zBf/SjH8Vzzz2Hr3/961i/fn09dk9AwBaV43eWf2oxqbGBgLcp\npI2UMGNhaTanNJXKo709hP7+OE6dSuCFF5Sgp+3bzRRZdvVxbCxlOkKDrMXlsnXwxFLDzIx1MclD\nZAcGlB5kJ0XWKezJOEOWEAr5uBRZ1pRZAHjNazYCUCzzgL0ia2ctJkW2UVbHW2/dgJtvXof/+T9f\niZMn34/77rMPh7KCXiVzR2R96usJ6XQB0WigguRYJRYD9nOGnWbQGtHWpozk0LszpqYW0N4ecuw7\n1iMW86vfZZ6+1q6uiKUiyxsEZKXIzs/ncPLkLLZu7TR72aLB2CPb2hrk+q4Byn1ydDSFYrHsaoYs\noKlrvb1RV9bkWiBJ0rlzXKnIasqfILKNApu1WCiyAu5Rl7vL7t278fa3vx0bN27EtddeizvvvBN7\n9+7F3r17cfr0aTz00EPYuHEj/viP/xhXXnklfvCDH5hu5+GHH8bPfvYzfO1rX8NFF11Uj10TEHAE\nKYR+/9KwFo+MzGPz5i/hBz84zP3aZ54ZwZNPDuHWWzegvz/eFButVY9sKFSt6FAxvWFDO0ZG5rFv\n3wj8fg+2beuq2i4rkV1YKCCZzJmqQ1T8s4QSLRVMTy9YFpNmvY9WsBu9A9gTFT1o9M6aNZXW4kiE\nzVpMq+92fa5G3HzzWgQCXvzylwOOryUngJlC1mgiu2FDO37+8z/CX/zFLteEAKhMhabvtNG6bwd6\nrjHsKRLxVdis7YgoLfKZLXTwKrJmfaXT0xnmoCeC/hjwEdkwpqYWqpJUUyn2ebQEqx7ZffvOolyW\ncf31/VzbqxWURSDLSi+7Gxt9NOpX+2tZrd5GUL9jo23FBLPFCmFhbTy0hR47a7FQygXcoy5Etq2t\nDT/96U+RzWYxPj6OJ598EpdccgleeOEFbNu2DeGw9gN+1VVX4cCBA1Xb+OY3v4l/+qd/wiOPPILt\n27fXY7cEBJhApFUf9mQ23L5ReOyxkzhzZg5PPTXE/dpHHz0GAHj/+69GNOpvirXYqtC2UmRjsYBq\nb3zppQls29ZVk43WbswJb8pvs7GwoBCXjg7zH3inhF49aLyRlSJLizisiqwx+Zi1/5hXvQMUkvJf\n/ku/+r20CwWxO8eatbgxYU/1glmPLE/YUzhcPR5JIbKBiv5Ju+MiSZJpYBvgRpFVzh/1yZbLMqan\nF5hH7xD05JUnoKmrK4JisVzVp5tK5bkTba0cAHv2KPfvXbv6uLZXKyiLYHAwiUQi60oRjkT86nfN\n7QIMWcQb1Y9uRGdnGKlUvmLRUiiyjQePIsuzuCkgQKgLkf3Yxz6Gffv2YceOHbjpppvQ3d2N++67\nD5OTk+jp6al4bmdnJ8bGxir+9uMf/xif+cxn8Na3vhW7du2qxy4JCDDDPOypeT2yTz89DMA+HMEK\npLpdfnkPYrEA0umC7fy2xYDWA1mpbBA5pcKiXJbV+Y8bN2rkysxWDCg9ceGwz5G0aeqQubUYYCN+\nSwF2QU+AvvfRWQl95pkReL2SqdoNsFuLz5wx75ENh33I50uOid9EeniDj267baP6b7uCh3on7a3F\nzSmu3cLr9SAU8hmIbGMVWaCy/1IPXiJL548K2GQyi1JJ5lZk9fcYXmsxUBn4VCqVsbBQ5LYWW82R\n3bNHaaHatWs11/ZqBX2PiUhfffVK7m3oFyHd9siuWBHD3/7trfjoR/+Lq9fXCrNQL6H8NR52qd4E\ncV4EakFdiOzp06dx8cUX47vf/S6+9rWvYWRkBJ/+9KexsLCAQKDyRyEQCCCf1wqMkydP4oEHHsDV\nV1+N73//+5icnKzHLgkIMEPfI6uFPTVPkd2zh4gsP9kaHEwgFPJhxYoYIhGlf6zRNlqrMB9KAiWi\npFduSZEFrIms8lznsCZthmw1kaV5pyzEbylgZkYhslaqiNfrQTDodSTmqVQezz03iquvXmlZqNNC\nA4siGw77qgpc1mPLa0Ml3HbbBvXfTtbAWMyeyC43RRbQgsoosIkvtbhakVVs/f4KRdbpnOj7L/Vw\nE/YEaAUs9TLyKrLurcXVRFZrieDtka0OeyoWy3jmmRFs29ZVk6XcDcj5QkR6x44V3NvQX1u17P97\n33sVrryS//3rAW0Ej9YnKxTZxoOIrJ21WOuRFedFgB/s3iQLDA0N4VOf+hR+/etfq+rrQw89hHe+\n8534wz/8Q6RSqYrn5/P5CqtxMpnExz/+cdx11124/fbb8cADD+DLX/4y8/sbibJANcQxskfpXF0W\njQZVa7EsS005bhMTaZw4oYTyzM/nufdhYCCBdevaEAoFEY8rPwqFgoTW1vp/Fqt9y2SK8HolxGLh\nijA3UpDKZeXYFgo0hiWErVu71eddfXWf5baj0QAymaLtcZmeVn4UV69urXoeHZN8Xm74+XXzfnNz\nCins7W2xfL3Sm1qy3f4zz5xBqSTjlls22BxbpeAolez3dWhoDmvWtCIYrCSTRABKJfvvzuSkUliu\nXt1m+zzjY5dfvhLr1rVhcDCBzs6Y7WtjsSDS6ULVc8bH0+jsDKOlpbEpqvWAElRWxMKCcsNqa4sw\nX1OtrcrnzWaV66RUKiOXKyEaDaKtLYr29hBmZ7Po66v+zuhBfdDG50xOLiAS8aOjg60fsqND2Z90\nWtnW3JxCsHt67M+rEfR9VrYZNV04NwO5NZJJ7RrJ58neGOLah85OZVv0WQDgpZfOIp0u4MYb1zT8\nPkMLSuTs2bVrLdf3DKics7pihfW9Zymjt1dZoNGf42Qyh1gsgEjEPWFajseiGaDj1NmpLG6lUta/\n28mkUgv09CzPa60euFA/dz1QM5F9+eWXEY/HKyzEl1xyCUqlErq7u3Hs2LGK509NTaG7Wytat2/f\njje+8Y0AgE9+8pN417vehUcffRR333030/vr1V2BahgVcIFqpNPKSqHXK6vW4lyu0JTj9pvfaCOn\nEoks1z4kElnMzmaxc2ffuQUj5bPMzqYRj9f8Va+A3XVFfa+FQqVy6ju3C/PzyueamVFUnEjEi/5+\nrQDetq3DctvKuI2U7XEZGVGsr52dwarnhULKQkUyudDQ8+v2ezg+rvSjtrZav556oe22/6tfnQQA\n3HBDn+XzPB7Fgp7JWG8rnc5jenoBV165wuTYKtdbIpFBa6u1Wjg2poTItLX5Ld/H6njdcccmfPGL\nz6GnJ2z7eaNRP6anM1XPOXt2HqtXx5flPTEaDWBmJqMqG34/+++f/9zpSKVyyOfz6jbCYS/y+Tz6\n+lowO5tFe7v9ddraGsTgYLLqOePjKfT0RJn3hyzB09Np5PN5jI0p13lbW/V31g6RiNZLHwxKFa+1\n+861tytEbXR0Tn0O3Y8Uizz7PtB6jv6e8sQTyn18585VDb/W/H5l8fDgwUls2tSOSMTD/T2j7zIA\nxOPW39OlDO0cJ3XneIH7GtND1FNs0B8n+s2dna38zT14cBLr17chEvFjdnbh3HOlC/L4iuuKDVZk\nv2ZrcU9PD+bm5jA1NaX+7eTJk5AkCRs2bMChQ4eQzWrz4vbv318R5uT1ajfMG264AXfddRc+9alP\nYWJiotZdExBgAlnlAgGvbvxOc3pk9+4dVv/N2yM7OEiptEqaLM/c1XrCaoQFWYupJ5n2KxoNIB4P\nYs2aOLZs6XCYEVqbtbhZx8QtnHpkAUWB0c8HNcPu3Wfg83lw/fXW/Xpmc36NGBpSSKgx6AnQzi+L\ntbijI1Q145cFn/zkTfjlL9+Ciy6yD7Aha7G+P5zSrJejrRiotBZLkna+WF8LaAFYlHJNf9+4sR2B\ngNcxzbm9PYS5uVxFH7Qsy5iYSKO3l13lJms4WT3pOudNyHVrLabvk348i1VvvxPoffXWYlJD7b5v\ni4VAQFu0vOoq/v5YoNJa7LZHttnQemS1c5xM5oR9tcGgfnj93Ojjx2dw9dVfx6c/vUd9LB4PNnxM\nk8D5gZqvmiuuuAJbtmzBBz/4QRw9ehQHDhzAgw8+iLvvvhu33XYb+vr68KEPfQgnTpzAV7/6Vbzw\nwguqAmuGj3zkI/B6vXjggQdq3TUBASZQT+BS6JF9+ulheL0SVq9ucUFkFSVy/Xql35QKrEYn9KbT\n5iMsqMDKZpXjbZw3+5OfvBE//OHv2247GvUjl7MPFBofV9oZ7MbvNGMskRs49cgCzuR+fj6H/ftH\ncc01K237/7TUYutjaxX0BOiDp+yPrUJ63I3kiEYDuPHGNY7P6+qKoFSSMTmp9UBSf2yzUlRrBc1A\nTqWUkVXGGex2oCReuqdo/enKOfvsZ1+Fxx57s+O8Ua23VStKZ2ezKBTKXCnUlDpNqcFENnhJk568\n8hDZ7m7rHlne1GKv14No1K8SWVmWsWfPMFaujFkmhC8m9AscbomsfkZxo3t86wUKDqNzXC7LSCaz\ngsg2GD09UQSDXnWhHQBefHEc5bKMffuUPm5lgUEkFgu4Q81E1uv14qtf/SpaW1vxjne8A/fffz92\n7tyJT3ziE5AkCX//93+PmZkZvOENb8BPf/pTPPLII1i50vrm2tbWhr/8y7/E7t278a//+q+17p6A\ngCPMU4vdEdk9e4bR3/+3ePHFcebXkPqbzRaxf/8Ytm/vxcqVMW4iq41XURRZKlIbPYInlSpYKLJE\nlIoV+0UKyLZtXdiyxV5pYxmfMz6eht/vUcc/6GGW3rqUwaLI0tgbq3Tqp54aRqkk4xWvWGv7Xlpq\nsZ0iq1hAzYis2YgXI/L5EmZmstxBT7ygkSNHjkyrf9OCnpoz17JWRKMByLJi0eOddRoO+xEMelUC\nSueI+inXrGllUg/pO0WzgAEt6ImHyFYrsgrZ4B+/ox0HHgJKJIfeF9AUVd6wJ0AJtCG79sBAAqOj\nKezatZprsaFeqAeRPR8U2Z4e5RxPTCjneG4uB1kWgUKNhscjYePGdhw/PqP+RlEOyOHDipMzkcja\njlQTELBDXRrnuru78YUvfMH0sf7+fnznO98xfezee+/FvffeW/X3O+64A3fccUc9dk1AwBFaanHt\nc2S//vX/xMREBs8+O4rLL7dO3yXMz+ewZcsj2L59Bd71ru3I50vYtWs1Dh+eQi5XQi5XVFMonUAr\nnqQCUEFmR/rKZRnf/e7LuO22japKUQvKZRmZTMG00DbOkdVbi1mh/0yUhmjE+HgaK1bETItISm9d\nLnNkWRRZSqfO5UqqvVeP3/zmNADgppvslUyW1GK6xtasaTXdD8DeWqyNaVncsCWyHh89Oo1XvEL5\n3DRD1sk+u1RBx3dyMuNK0e7oCKsElIisXnljgZki62acEn13SZGlBRte9c+ttZjudXrFnu5HvIos\nvTctPB44oCxiXnvtKu7t1AN0n/V4JFxxhfNvkBno/h2LBZh/f5YaaGGFrk8tsVgof43G5s0dOHRo\nChMTGfT2RnHixAwA5bd6YiKNubmc6cKzgAALhCFd4IKH3lpcyxzZYrGMf//3EwDY+1uHh+cxM5PF\nr389iHvu+TEApa9Km7PIrsrSDNl164jIOtton3lmBO9617/ha197nvl97KAfqWMEKQU0DshqTI8d\nSIGx6nGVZRljYynLoppF0V1KYLFcOtmld+8+A7/f4zjPUuuRtb726RrTj0siEIm2u9545426xUUX\nKbNyjxzRshu0GbLLk8jS93lhocityAIKCaVinq5/3u2YKbJ0TnkWwogQa9ZiUmR558gq945QSGsL\nYUFLSwB+v6diNIux1YEH8XhAVXRPn1bs92bfkUaAvscXXdTp6rMA2v17uaqxgLIo4vN51FYTMXqn\nedi8uQOA0hur/y8A7N07Alm2nw0uIGAHQWQFLnjow55q6ZF96qkhzMwoP5Z2M9P0IEJ2+eU9CId9\n8Hol7Nq1mmmIuBHKWJKw+loifXbWYtpfNzNrzWBHTrWwJ2OPLM88THvSlkhkkcuVLIksFWhOfZxL\nBTMzC/D5PLYqEZERIzmfns7gT//0MezfP4prr13lqL6RIutEZAMBb8XsUQJt325usdsZsrzYskUp\nnM4va7F2/shZwIP29hASiZzqmgA0azErzBRZN+dUU2S1sCePR+ImGXTv4FVRJUlCd3ekQpGtxVrc\n0hJEJlNAsVhW7fdmgWiNABHZq692ZysGtO/yciayHo+E3t4oxscwOrFyAAAgAElEQVSVc0wziwWR\nbTw2baoksmQtBpR2LECcFwH3EERW4IIHKbL61GIWa3E2W8Rf//VvMTysFC4//elx9TFWYkhk7nWv\n24L//M/34PHH70FfXws3kS2XZQwOJtXEYoDNWkxE2s5OygO75E8iSkR0arEW03EzghKLrayXy81a\nPD29gM7OsG2vHZERPbl//vlRXHLJV/DlLz+PzZs78NnPvsrxvchCaHctKHOKW+HxWNu27RVZpah0\nG/bEitbWEFatiuHYMY3Ini/WYsAd2WprC6FcljE/n6sKe2KFuSKrnFMelZ0WZxKJHGRZxsmTs+jr\nazG9ruxAx8HN8ejsjFQosrVYi+k1qVReDURrFpGlBcMdO9wTWboulmvQE6GnJ4rx8RRkWRaKbBNB\niuyJEzNIJLKYnMxg1SrlPrxnzxAAocgKuIcgsgIXPMzDnpyJ3S9/OYBPfOJJvOlN/4pCoYSf/OQY\niG/wKrKRiB/r17fhuusU+yevtXh0NIV8vqTaimmb+vcwAxFCOxWOB3a9d1RgEVFyYy3W1Efzz0Tq\nkNWIFRZyv5QwM7PgqIqYWci//e0XMTubxcc+diOef/7dTEWtU49sMpnF9PSCZRIrEWq7Hlmy+S12\njywAbN3ahTNn5tTrbPlbi7XvCW9vK6CR0JmZ7KL0yPLaxcnqfPp0EuPjaVxzDX9PKd073JDPrq4I\n5uZy6v3f7fgd/fvPzeUwNDSHSMTfNBL4+tdfhHe/+0q86U0Xu94G3VOWsyILKC6BhYUi5ufzoke2\nidi0SbHZHz8+o/bHvva1m+DxSHj++TEAYoFBwD0EkRW44KGFPfm4FFka4v3ss6N4+9t/gtOnk7jp\nJiUZlpWAEqEykjkag8Gq7BqDnvTbtCNtVLzZJdXywK7PjIo9OjaLYS22G72jf/1ysBaXSmUkElnH\nYtLsmJB16777rmGe1+pEZLXxTlZE1lmRbZS1GAC2blVUAFJlT51KoL09ZBqItRxQaS121yMLKCTU\nbY8sbcMstZj3nMbjQczN5fDss2cBuAtHcmstBrSEZFJlyVrspq+U7tekyPb3x5uSWAwAfX0t+NKX\nXoP2dvcklK6v5a7I0jU5Pp5Wr9m2tuX9mZYjenujaGkJ4MSJWdVefNllPdiwoU1t4xKKrIBbCCIr\nsOywsFBQ01zrAX1qMU+PLJFMSQJ++MMjAIC3vOXSc4+xEVkrFYBu6nNz2arXmMEY9ARY907qQcpm\nvRRZO5XVmCLpzlps/5nIWmzVB7mcwp5mZ7OQZedi0kyRPXFiFitWRB3ngurhFPZE1xjNKTaCFFm7\nHtlGhT0B+sCnaRw9Oo2BgQTTDNqlikprsXtFdnY2q+uR5SP1VopsIODlLkTb2oJIJnN4+mllluTO\nnX1crwf01mL+40HBUhQ0Rfcjd0RWec3oaAozM9mm2YrrBRqvtXFjcwKr6gU9kaV+bKHINh6SJGHz\n5g6cPDmLY8cUIrt5cwcuvrhbfY5ILRZwC0FkBZYN9u4dxnve82/o7//f2Lz5EVURrRVu58gSWf3Q\nh66HJCl9X6973RYEAl51dd8JVsRPI7LuFVknG67y/kpBW68eWY2cVheW+qJCee9arMVWiqy9OsRC\n7pcKWGbIAtVKaD5fwpkzSTVggxVOiuzJk4rKa6XI8vTINobIaiN4qH/9zjs3L/r7Lhb0Cz68BBTQ\nCsVEImubLs6yDWNqcW9vlFuBbG1VenYff3wQPp8HV17JPyqG7h1Wo7jsQIosJYPXosjS+x88OAkA\n6O+vHk+1nHD55b14+ul34I//+Mpm70pNoN+BiQm9IisIUzOwaVM7stkinnjitPr/F1/cpT4u5sgK\nuMXy9FgJXHBIJrO45ZZ/Qqkkw+uVUCrJGBhI1GSfIujDnopFpRhj6ZEli+ztt2/C5s0dKBTKaGsL\nIR4PMCuyVr1q+p4rFmiKLF/YE5HJeiuyZgVyLBZAJOJXVTk31mKnz3Q+WYtZZsgCmvpMYz9OnUqg\nXJbVviRWaIqsuaKqjd5x3yM7NpZCR0dIJc2Lia1bFSJ75Mg0xsdT8Hgk/O7vblr0910s1Br2ZKbI\n8qYfG4msLMsYH0/j0ku77V5mClLGDh+ewo4dK7gTlAFg9eoWfOQj1+PVr97A/VqjIlvL+B16zaFD\nyrin5a7IArWFRS0V0O/A2FhK/b0WRLY5oMCnvXtHEAx60d8fxyWXaPcNcV4E3EIQWYFlgZmZLEol\nGa9//VZcddVKfPSjT6jhLbUilyshEPBCkiRVkWXpkSWSGY+HcM89l6l/b2kJciiy5sUTb9jT4GAS\nHo+ENWv0RNZ5/A4VtFbkhRdaz695UdrTE62yFvP0+9mpzPPzOezdOwJJsrYWezwSQiHfslBktRmy\n9sFIl13WAwB48cUJAFDDNNwqslZuBM1abN8ja7dIMDGRbogaCyihTvF4EPv2jWBsLIUbbujnnlO6\nlKB3ObidIwsoJFTrkeUjbX6/F9GoX7UWz84q467cjDTSq6hu+mMBxbL48Y/f5Oq1tM+UZp1O5xEI\neF0tssTjRGQVRfZ8ILLnAygdfXw8LcKemgz6PSqXZWzY0A6v11OhyIrzIuAWwlossCxAxXF3d0RN\nHaV+yFqRz5dUNYp6ZHmIrLE3jEJMWGBlr9XCnpy3s3//KA4dmsTq1S0VRZhZ76TV+9dv/I59gdzb\nG8HERAblsoxUKo9IxA+vl/02ZGUNLpXKePvbf4ITJ2bx3vdeZUuOo1H/siCypMg6WYu3bOlAOOzD\ngQPjALSgJ15F1uv1wOuVbBXZnp6IpWJlNgZIj0KhhJmZbEOCngCF5Fx0USdGR1OQZeCOO5avrRgw\nWovd9Mgq19Hs7IJ6P3VrUSZFlhYTV62qnivsBL0C46Y/tlZQsjl9hvn5vKvEYkC7Xx88qCiyy91a\nfL5An8uQSGTh9UquFHeB2kGKrP7fW7Z0qjWXsBYLuIUgsgLLAkQ8IhF/1Up6rcjlNCLr8UjweCSm\nHllSS6uJbADz83mUy7LjNqysxVqPrDWRTSazuPvu7+H667+FmZksfu/3Lqp43Ov1OKqP9bYWOwWm\n9PREUSyWMTu7gFSqwF04atbiSkX2r/7qCfy//3cCt9yyDg8//DsO2/DbkvulArI8OhFZr9eDyy7r\nweHDU8jlimoqJK8iCyiqrNmiRqlUxunTSUs1FtD3yJoT4Ub2xxLIXgws7/5YoL5hT24VWUAbmwMA\nIyPzAJSkXF7oC1e3imwtWLlS2We9IssTjqaHfo4soIUlCTQXtFgxNpZGIpFDW1uoaWnSFzr0C6v0\n70DAq/5bhD0JuIUgsgLLAlo4iV9VZOtnLS5WKJl+v4epR3ZuLgevV6pSNfSjGJzgFPZkZy3+1rde\nxM9+dhK7dvXh5z//I3z607dUPScW89vuBxW09VNk7WcxEokZH08jnc5zF9Jmo2ZOnpzFF76wD5s3\nd+C7373bcdxMONwYIivLzgsZdqCwJwqlscP27b0oFss4fHhKtRa7SRwNBn2mixrDw/MoFsu2RJbO\npdvRSIsBCnzatq3LFbFfSqgcv8OvpOoTh932yAJKwZlM5lAqlWuazUtWws7OcFPScYnknD2rfIZU\nKu9qgQCoHP8jSUrvrkDz0doaRCDgVRVZ0YfZPLS3h9XfMv29+B3v2I677triaoSWgAAgiKzAMkGl\nIqsFONQD+XwJgYBW0Pn9XubU4tbWYNUKL/VLsdiCrYifYrmVMD9vvQ1S3v7u716Dm29eZ7rSHI0G\nmKzF9Qt7ovNk/qNE5258PH1OkeX78aLn63uQh4fnAAB/8AfbmMK/GmEtfvrpYaxY8TfYvfuM622w\nhj0BCpEFgAMHxnHy5Cz6+lpczRoNBDymixpO/bGAEhbl8Ug2RLZxM2QJ1D98111bGvaeiwX9ok+t\nYU8UyFXLPNpkMoezZxVFthZr8TXXrGqKShYM+tDZGTZYi90V03old9WqFubZzQKLC0mSsGJFVO2R\nFX2YzQVZivU24//xP3bie997g1DKBVxDEFmBZQHq6YpE/OjoCCMQ8C6KtRhQFFnWHtl4vHqFl4oa\nlsCndLoASarueZMkCfF40FaRpXEoVimygHK87BRZjcjWJ+zp2LFpSBLQ12eu0Oh7ltJp/p40KkQS\nCe24kM2RtccmElEU2VoVUzscODCORCKLhx560vU2yFrMElC0fbtC2PbtG8HQ0Bx3fyzBSpF1miEL\nKNdsJGK9SNAMa/Gtt27A9773Bnz4w9c37D0XC3rS6YaAhkI+hMM+JBJZpNN5BINerv50gn6MTy3W\n4rVrlT7SV75yLfdr64WVK2MYG0shny+hUCjXQGS11wlb8dJCb28UZ8/OY2GhKBTZJuO661YjFgvg\nkku6nJ8sIMAIQWQFlgX0iiytstYr7MlIZH0+D3OPrLE/FtDSOFkU2XS6gEjED4+nejXSKTTq5MlZ\nrFoVs1VnYjF79bGe1uJCoYT9+0dxySXdlr1mpMYNDc2jUChzK0vBoA+RiJaaCmijQFh7bLQRPPUh\n72YgVXL37jN47rmzrrYxM7MAj0diKr4uvbQHHo+En/zkGAB3/bGAta2eRZEFSO02XzihtOpGEllJ\nknDXXVtchSMtNVRai919HgpqymSKrrehtyjXYi2+6qqV2LPnHbjvvmtc7Uc9sGJFDMlkTr023RJZ\nfQKzSCxeWujpiaJUUhYtBZFtLj7xiVfg8OH3OibxCwjwQBBZgWUBfY8soBQgY2OpuqhquVyxSpF1\n6pEtlcqYn8+rNmI9eK3FVsWTQmTNSUEuV8TQ0Jxjb1k0GkAuV7JUmOtpLX7ppQksLBRtE0iJxJw6\nNXtu/9yF1hB5BcA96J4lzblWZLMaSf785/e52sbU1AI6OkKmixxGRCJ+bNnSoY7scUtkrRRZOl92\n6j/tx1KyFp9PqDXsCdCCmtLpgutt6C3KZ8/OIxTyuQ5queqqlWpqaTOwapVCwKlNw22fnv4eLhKL\nlxZoBA8giGyzEQz6GrqQKXBhQBBZgWUBUg5JWentjaJQKKt9hG4hy/I5RbayR9bJWkwEUL8ST+Cz\nFlsHjLS2BjE/nzNNPx4YSKJclhmIrPXcVVmW6zp+Z98+RXlkI7KKwudGAWlrC2F2VjvvpM7yKrKL\n2SerX3j50Y+O4uTJmYrH//Zvn8FvfnPadhszMwtM/bGEK67oVf/t3lrstbQWBwJex15Iu/7jZoQ9\nnU/wej3qglstiixZi92q1EZFduXK2LLtb6PkYiKybsm9x6ONdRHW4qWF3l5N/RNEVkDg/IMgsgLL\nAtQjq1dkgdpnyRIR1luEfT7nHlmr0TsAnyKrKCPWiqwsm6cfEzFyUt6s5q4CirWWBO16KLL79o0A\nsCeyRGJqUWQ7OrTUVMC9tXgxFVmyLb/vfVehXJbxhS/sVR9Lp/P44Ad/hc985mnL15fLMqanF5j6\nYwmXX64RWX2YBg/Mxu/IsowTJ2axfn2bozpsN9poZkY5T07jhASsQd+XWmzBsqz0X9eqyE5OZjA+\nnnbVH7tUQOFzRGRrmTFKaq6wFi8t6BVZMeJFQOD8gyCyAssC+h5ZAHUbwXPkyDQA4KKLtPABlh5Z\nIqlmiiwFQDkRWVJErQKPiBCbBT5R0JOTIkuFmRkZ1pPbXK5Ys017374RtLWFsGWLNYlqaQkgFPJh\naGiuYv94oE9NBTRFdilZi2nh5Z57LkMsFsDu3Zr6SsfdLoQrkciiXJa5SB8lF0uScy+rFYJBhcjq\nrwVlBmMW27Y5B3REIgEUCmVTaz79TT/qSoAPtOhVKwmV5drIMAAcPTqNcllW7bnLEeQwqNVarH/t\nmjXCWryUQIsVgPnCs4CAwPKGILICywJmPbJA7SN4Dh+eAgBs29ap/k3pkXUistbWYiKgTtbiXK6E\nUkm2VWSV93JPZO3URz2RkmUwJTVbYXIyg1OnErj22lW2qp0kSejtjap2aTdEVt+jByxNazEpsrFY\nAPF45Qgk+rcdkaUZsm6I7Jo1rQiF+OeDAlDHhuiv/0OHJgFUfkesQHNJzY5toVCGxyO5SsoVUEAz\nq91bi7XrqVYie+iQcu8ke+5yhFGRdTPWiEBEVliLlxb0PZnCWiwgcP7BXbUjINBgUPFPfV31miWr\nEVlNbVLmyNpbbZNJGvniPrWY+lat1BW77Zw4wRa+Q4WZGbEw9s3mciXX8w81W/Eqx+f29kZx+nSy\nYv94QMW40ifbjkQiC5/Pw6xS2fUN1wt0vUYiPoTD/oqEZPo3zdw1w/S0MqqGJ92xuzuCe+651HXQ\nEwC1BzOXK6rKKRGWiy/udny93spuLBoLhRL8fkFia0E0Gjg3rsvdT7d+sad2IqsscLiZIbtUQCSc\nUrlrsRb/3u9tw4YN7YIsLTHoe/LFuWk89u/fj1KphE2bNqGjw/q3aWZmBidOnIDP58OOHTsauIcC\nyx2CyAosC2QySvFPhXK9rMVEZLdu5VVkra3FtDJvlThMICJjVTwRSbayFq9YEbUcc0Mg27KZ+mf8\nWy2BT8884xz0RNCvkPPOkQWqFdnZ2Sza20PMgTONGL9D2w6H/YhE/KrCqjymnHc7Ik3P5wl7AoCv\nf/1O3l2tAJFX/bVgtthjBW3hpPqz5fNlYSuuEbffvgn9/XHX4Ur6Qr5WezLNBbaaGb0cQAuiNJ6l\nFmvxBz5wXV32SaC+qCSywlq8mCiXyzhx6CByySRyOaVuyQaUY37wt08iHLO+V2Tm5+Hp7EI+n8dz\nj/8KAODxeBDr6sKmiy+BxyMWQQXMIYiswLIAFcZEQuoV9nTkyDTWrIlXEMLaw54WV5HN50s4fTqJ\nXbucSaO9tbjyb7UEPpEie801zopsJZF13yNLluJEIsvV+6RZixdPkSWyGgr5EA77Ko4/kVw76zkR\n2a6uxgYjaYpsJZH1eCTb3meC3fUmFNna8cADN9b0er0i6za12GjhX87W4mDQh87OsPp9c0vuBZYu\nYrGAOhZMKLKLi2d+80vs+PynIJVKAGQUiyXsf/AzAIDL//EriGTmLV+bjsbx8p9+BABwyacfgM/n\nBSBB9nrxzJ//Ja575asb8AkEliNEVSGwLEDFPxXKPT1RSFJt1uJkMouRkfmKoCdAUWTLZdl07A2B\n1FYzRZSVyJIiajdH1mw7p0+zjd7Rb9s87MloLXanUM7N5fDcc6PYtq2LqVDQj0NwYy3u6NAUWVmW\nVUWWFXZJzvXCwkIRoZAPHo+ESMSPfL6kpiwTyctmi5YLJjQPttGD442KrCzLOHx4Chs3tleMqLKC\nFqRVfS0VCmXX1nWB+qAeimww6KuwNi/nsCdAc/cA5vdzgeUNymUAhLV4sbHhW1+BVCoCUGqnl4an\n1ceOjM7avvbgiDai7vBZeq4MqVRE/z99o967KnAeQRBZgWWBTKaAQMALn0+5ZH0+D3p6ojVZiymx\n2GiZpPew65MlcmmmBIbDPni9EubnnRTZygArI4jIGq3FrEFP+m2bkTYitxTO5EaRnZ/P4XWv+xek\nUnncddcWptfUai2mYmR2NotMpoBCocxVoGiq4eJZizOZghp8REU/Lcbo39dKFaaxUI0eVRMIKPtK\n18LYWBqzs1lcfLGzrRiw7z/O50vCWtxk1KNH1rid5dwjC1QSWTf3I4Glj1WrYvB4JEFkFxntkxMV\n/18qyyhPTUFOJlHK2TugSvk85Pl5lMfHUTJMUOg5O1z3fRU4fyCsxQLLAsq81coio7c3qpI6N7Dq\n/dMntwYtFujtwp4kSUI8HmTukbVSJWnbRkJ84gTbDFnAPqGX3r+jI4SpqQXuHtl0Oo+77/4+nn56\nBG9848V48EE226O+Z6nW1GLeGbKAnsguprW4qFo36b+ZTAGxWADZrHYuUqkCWlur910Le2o0ka1c\nxOHpjwUqP6sRxaLokW026qHI0nbOnk2hoyPkOiF7qUBPZGtJLRZYuvjrv74Zp08nxf2nwbhsdQde\n+sTHUJSBizvsf6Mva/Hh4F9+GD4A21Y6L9ILCBCW9y+QwAUDReGqLLxWrIjhxRcnzs1i5S9AKI3V\nOFaE+vjs+mTtwp7o786KLJu1uBZFlrZtppCRItvREXZFZL/0pefw298O4Q1vuAjf+MadzGNV6tkj\nyztDFmiUtbigfjZSZonc6RVZqxE8bsbv1ANkHyZFlpJp+RXZ6mObz5dED2KToQ8Pc5t8DGgLR8td\njQUqe3xrCXsSWLq47rrVuO661c3ejQsOwYAfV69ku+fHwgHsDIvvnwA/BJEVWBbIZApVpFGfXLx5\nM//IEVKbjD2ymrXYjshaz5EFlIJoeNg62ABgtxbPzeWQSGTxiU/sRkdHGL/97RAA59E7+m3bjd+h\n4pbXWkzW7P/1v25WjxkL9IqsG2JD+zs7m0UioZB8/XxMJ5DqYqYa1gsLC0V0dyufk1RKshZTEBRg\nbS2enl6AJPEpzfWAsUeWZ/QOYH+9FQpCkW029NdTLeojLRzp1czlCkouBmobvyMgcKHDs2ZDs3dB\n4AKEILICNeHZZ89i9ep4TQXNAw88gWjUjw9/+AbL52QyBTWpmKAlF7sjskeOTGHVqliVmkeKLEuP\nrNUKvmItnoIsy5ajMkiNsyoo43FtjM8XvrAPjzyyX31sxYqoqSXVCNq22cxSIhsakeXrGR0enoMk\nAX19fKpMrdZirUd2QbUW8yiypEQtNpGl99HG/Zgpsub7MD29gPb2ELPKXS8YU4uPHFESi1m/X5GI\n9dxiJbVYENlmIhDwqgmu5BRwAyLEvN/9pQi9qiwcAwIC7hG+9y8XbduL1wgksNwhiKyAa0xOZnDz\nzd/B7//+NnzrW69ztY1kMovPfnYvurrClkRWlmXTHllaSXeTXDw/n8OZM3O45ZZ1VY/pe2St9zuH\nlpaAJdFoaQmgXJaRyRQsiapmLbZXZMfHU3jqqSF0dobxjW/ciRMnZnD55b2W+6aHliJrZi1WyAbZ\nV3kV2aGhOfT2RrlVtng8iGDQi1yu5IrI+nwetLQEzimyigWXL7XYmmzVA6VSGfl8SSWwGnFWCGxl\nj6yVIptpeGIxoCeyRciyjEOHlMRi1j5Iut70qjNBCXsS+YLNRnt76ByRrV2RPT+sxcqCaCTib/jC\nkYCAgIBAbRB3bQHXOHBgDIVCGSMj9hZaOzz55BDKZRkTExlLRTCfL6Fclqt6uqgA+c53XsKll34F\nV131D+qIEyccPWqeWAywKbLJZM52dqlmC7ZeRyQiaUXm/H5FPdm37yympxfwnvdcide8ZiPuvfca\nvOIVayy3q8diWYtlWcbIyDxWr44zv4ZA4xD8fo9rq2lHRxiJRFanyPLMkV1cRZYsxET+jLNVnVKL\nZVnG9PRCw2fIAtoiTj5fxvi4kljMGvQEWF9vsiyL8TtLBERC66HInh/WYuUziMRiAYHGIPvPX0P6\ng+9G/qnHm70rAucBBJEVcI0XXlCi1olMuMFvfnNa/bdVT6nWS1pJ+KgAeeyxUzh+fAYvvzyJmRm2\nfbFLY6VV+WLReo7s/HzOduYgPWY3S5ZIjJ2djezFfr8H733vVZbPs4LdHFkt7EkpSu2IuxGTkxnk\nciVXRBYArr22D5df3uPqtYBSjCuKLL+12Egs6w3arlGR1azFlanFRiSTOZRKckUwT6NAimw+X9T1\nx7ohspXXW6mkfJd4eqkFFgdEQmsZv0M901dcweYMWcogZ4/ojxUQaAw8HWyZCwICLBDWYgHXePHF\ncQDaKBo3eOIJjciOjMybJvFqxKDyct2xYwXe8pZLsWZNHMeOzeCHPzyCyck0urudLZmHDzsrssWi\nObGTZRnJZM62b5AIqF1ysRVBr9xOEGNjabzxjRe7Uj8CAS+8XsmUtBGJoqAknh7ZoaEkAGD1anfW\nwn/8x9dZ9g6zoL09hPn5PCYnM+r/s8Lr9SAU8i2atZgU2eoeWbIW26cWT00pn6mrq/HWYn3Y0+Bg\nAgBbOjbBavwOhUeJsKfmgxZ9aukHff3rt2Jg4N7zwlocDPpw6aXd6O93tygnsPSQyWRQLpcRizn/\nZqZSKXg8HkQijb/fXrCILv/7hsDSgSCyAq7x4ouKIkvJsbyYns6o2wCU8CAzEOEw9nQFgz584xt3\nAgAeeuhJAMDERAYXX+z83poi21n1mFOP7MJCEcVi2TKxGGC1FtuP3wE0gnb//ddaPscOkiQhFgtY\nhD3lEQh4VUsdj7V4aEg5V24V2Vp70ei4DA4qhJp30H006rfsT60VROK0ObLG8Tv2qcUzM0rfbzMV\n2VyuhNFRpfech6xYWYtJ7adFIoHmoadHKdjt7l9OkCTpvCCxhCeeeKvojz2PcPDgQZRKJWzatAld\nXdaOksnJSZw4cQI+nw/XXHNNA/dQQECgXhBEVsAVFhYKap/p3FwOpVKZuxDYvfsMAOC66/qwd++I\nJZElS6ZdTxepsJOTaab3Pnx4Cj09EdNAHaceWbILs/TIsimy1srIAw/ciFOnEti+3b2FLxr1W86R\njcX8VbNDWXDmjEIgm6ViEJEdGEhU/D8rOjrCNVni7UCKq1GRJQJLyixgbi2emlKIbDN6ZPWK7Nmz\nRGTZnQBWRDafVxaFRI9s8/GBD+zCtdf2YcMGdqX9fIddm4jA0kaxWMTzv/oFcqcHUSoo9x3f9h0A\ngBe++x14vdb3nFKphOCOa1AqlfDEl/43AMDr9yO4bj123PIq+HyiRBYQWOoQ31IBVzh4cBLlstZD\nmkzmmBSkTKaAfL6EtraQait+y1suxd69I6rKZwSLBbenR+lzmpjIMO3D4GDCMjCJ+visemSTSRq9\nw6LI2vfIShKqQqz0ePWra5/LFo0GTPcjnS4gFguo5IVPka3NWlwrSIE9fToJj+f/s3ffYW7VV/74\n31ddmt6by4wLtjFgm2IwxrQQUmxif+MNhGwgCewmQAxks7thKc5DCnEoIdkNGEL5wRechASHLw5k\nIYRqkmCIjQu2cZ8Z29Orpkgatfv74/JRL/dKmtFI8349Dwr/0wgAACAASURBVA+2ytXVHY18zz3n\nc46k+US0stKKY8cG4PfL0OlSL3GOJXqNbHhpcWhGNtaFjr4+5TOcna7FwYsaHR3Km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J3K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\nmhVJZIUQQgghhBBCNCv/B+/4676rtTMsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ee20310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a plot with the total number of trips per day, highlighting some changepoints associated with major holidays \n",
    "#and other weather and touristic/cultural events.\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "ax = byDate.plot(figsize = (16, 8), fontsize = 12, ylim = (10000, 170000), color = 'navy')\n",
    "\n",
    "formatter = FuncFormatter(thousands_format)\n",
    "ax.yaxis.set_major_formatter(formatter)\n",
    "ax.set_axis_bgcolor('#F9F9F9') #make background color a lighter gray to better contrast with the icon's images.\n",
    "\n",
    "plt.title('Total Trips per Day with Annotation of Some Major Events and Holidays', fontsize= 20, color='navy')\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.xlabel('')\n",
    "\n",
    "\n",
    "#Add icon to indicate snowstorm on the plot:\n",
    "#NYC's mayor ordered the city under curfew, and public transportation system was shut down late on 1/26/15.\n",
    "img1 = read_png('Data/icons/Snow-48.png')\n",
    "imagebox = OffsetImage(img1, zoom=0.6)\n",
    "xy = ['2015-01-27', 25000] # (len(byDate[:'2015-01-27'])-1, byDate['2015-01-27'])\n",
    "\n",
    "ab = AnnotationBbox(imagebox, xy, \n",
    "                    xybox=(22., 10.), #position from xy to lower left corner of box\n",
    "                    xycoords='data', #will use data coordinates for x and y\n",
    "                    boxcoords='offset points', #same idea as textcoords, in points from lower left corner\n",
    "                    pad=0.1, frameon=False) #padding around the image\n",
    "ax.add_artist(ab)\n",
    "\n",
    "\n",
    "#Add icon to indicate Thanksgiving on the plot:\n",
    "img2 = read_png('Data/icons/Thanksgiving-48.png')\n",
    "imagebox = OffsetImage(img2, zoom=0.6) \n",
    "xy = ['2014-11-27', 40000] \n",
    "ab = AnnotationBbox(imagebox, xy, xybox=(5., -5.), xycoords='data', boxcoords='offset points', pad=0.1, frameon=False)\n",
    "ax.add_artist(ab)\n",
    "\n",
    "#Add icon to indicate Christmas on the plot:\n",
    "img2 = read_png('Data/icons/Christmas Tree-48.png')\n",
    "imagebox = OffsetImage(img2, zoom=0.6) \n",
    "xy = ['2014-12-25', 25000] \n",
    "ab = AnnotationBbox(imagebox, xy, xybox=(10., 5.), xycoords='data', boxcoords='offset points', pad=0.1, frameon=False)\n",
    "ax.add_artist(ab)\n",
    "\n",
    "#Add icon to indicate Memorial Day Holiday on the plot:\n",
    "img3 = read_png('Data/icons/Leave-48.png')\n",
    "imagebox = OffsetImage(img3, zoom=0.6) \n",
    "xy = ['2015-05-25', 60000] \n",
    "ab = AnnotationBbox(imagebox, xy, xybox=(1., 5.), xycoords='data', boxcoords='offset points', pad=0.1, frameon=False)\n",
    "ax.add_artist(ab)\n",
    "\n",
    "#Add icon to indicate Pride Week on the plot:\n",
    "#According to The Wall Street Journal, the Pride Parade was attended by 1.6 million people in 2015.\n",
    "img4 = read_png('Data/icons/Rainbow-48.png')\n",
    "imagebox = OffsetImage(img4, zoom=0.6) \n",
    "xy = ['2015-06-27', 150000] \n",
    "ab = AnnotationBbox(imagebox, xy, xybox=(18., 2.), xycoords='data', boxcoords='offset points', pad=0.1, frameon=False)\n",
    "ax.add_artist(ab)\n",
    "\n",
    "#Add icon to indicate Independence Day Holiday on the plot:\n",
    "img5 = read_png('Data/icons/Leave-48.png')\n",
    "imagebox = OffsetImage(img5, zoom=0.6) \n",
    "xy = ['2015-07-03', 70000] \n",
    "ab = AnnotationBbox(imagebox, xy, xybox=(5., -10.), xycoords='data', boxcoords='offset points', pad=0.1, frameon=False)\n",
    "ax.add_artist(ab)\n",
    "\n",
    "#Add icon to indicate NYC Marathon event on the plot:\n",
    "img5 = read_png('Data/icons/Running-48.png')\n",
    "imagebox = OffsetImage(img5, zoom=0.7) \n",
    "xy = ['2014-11-01', 110000] \n",
    "ab = AnnotationBbox(imagebox, xy, xybox=(5., 7.), xycoords='data', boxcoords='offset points', pad=0.1, frameon=False)\n",
    "ax.add_artist(ab)\n",
    "\n",
    "plt.show()\n",
    "#plot_url = py.plot_mpl(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The effect of time on demand for Uber rides: distribution per hour, weekday, and month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekday_labels = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "month_labels = ['Sep', 'Oct', 'Nov', 'Dec', 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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PqUzB0NvbW97e3tauBQAAAABQAcoUDDkzCAAAAAC3rjIFQz8/P5lM\nJot5devW1datW61SFAAAAADAdsoUDI8ePWr++9q1a9q8ebP2799vtaIAAAAAALbjUN4VnJycNGDA\nAMXExFijHgAAAACAjZXpjOHatWvNfxuGoePHj8vJyclqRQEAAPuTm5ur+Pg4m/bp49NMjo6ONu0T\nACqjMgXDXbt2WUzXqlVLCxYssEpBAADAPsXHx+m58Ai51r3NJv1lXDyv+ff2la9vc5v0BwCVWZmC\n4bx583Tt2jWdOnVKubm5at68uapUKdOqAAA7xhkglJdr3dvkXr9hRZcBAHanTOnuxx9/1NNPP62a\nNWsqLy9PSUlJWrRoke666y5r1wcA+BOLj4/T0+vfkYu3p036y7yQooXBEzkDBABAOZUpGL788sta\nsGCBOQju379foaGhWr16tVWLAwD8+bl4e8q9fp2KLgMAAJSgTKOSZmRkWJwdbNu2rbKysqxWFAAA\nAADAdsp0xtDDw0ObN29W7969JUmbN29WzZo1rVoYAACwPu4DBQBIZQyGoaGhGjdunKZOnWqe98UX\nX1itKAAAbmWVKYzFx8fpmfD/yrVuXZvUkXHxot669z7uAwWASqZMwXDbtm1ycXHRV199pdOnT+vZ\nZ5/V7t271bRpU2vXBwDALSc/jK2QS13b3HuZeTFRb907otgw5lq3rtzrN7BJLQCAyqlMwXDlypVa\ntWqVXFxc5Ofnpy+//FLDhw/XiBEjrF0fAAC3JJe6deRe3zbP6wMAoDRlGnzm2rVrcnJyMk9f/zcA\nAAAA4M+tTGcMe/furUceeUQDBgyQJEVERCgoKMiqhQEAcDNVpvv6AACobMoUDP/xj3/om2++0Z49\ne1SlShU9/PDD5hFKAQD5bB08CB3lEx8fp6fX/Ucudb1s0l/mxSQtHPgIg6wAAP4UyhQMJal///7q\n37+/NWsBgD+1+Pg4TV8/Vu7eLlbv6/KFTM0OfpfQUU4udb3kXt+7ossAAKDSKXMwBACUzt3bRR71\n3Sq6DAAAgHIhGFYg7ncBAAAAUBkQDCtQfHyc4j99R41re9qkv9PJKdLfJnLpGQAAAAALBMMK1ri2\np3y9bfOAYwAAAAAoSpmeYwgAAAAAuHXZLBgePHhQ3bp1K3Z5eHi4evfuLX9/f40fP17JycnmZX5+\nfvL391dGRobFOjk5OQoICOCZigAAAADwB9gkGK5evVqPP/64cnJyilx+9OhRzZw5UwsWLNCuXbvk\n5eWlKVOmWLRxdnZWZGSkxbzo6OhitwkAAAAAKBurB8MlS5Zo+fLlmjBhQrFtCs4WtmnTRlWrVtXz\nzz+v6OhopaSkmNv069dP4eHhFuuFhYWpb9++VqsdAAAAAOyB1YPh0KFDtXbtWt15553FtomLi5Ov\nr695umbNmvLw8FBc3G+PcggODtbu3buVlpYmSbpy5YpiY2PVq1cv6xUPAAAAAHbA6sHQy8ur1DaZ\nmZlycXGxmOfi4qKrV6+apz09PdWhQwdFRERIkjZt2qTAwEA5OTnd3IIBAAAAwM5UilFJnZ2dLUKg\nlB8W3dzcLOYNHDjQfDlpWFiYBg0aZLMaAQAAAOBWVSmeY+jr66tTp06Zp1NSUnTp0iWLy0slqU+f\nPpo9e7YOHTqkhIQEtW/fXlFRUWXqo1YtV1Wp4ihJSk11V0op7W8mT0931alTvdD81FR3JdmwjpJq\nAfDHpaa627S/kr5bbI1aqIVarF1LeiWpI8tmdZReS2qlqeVMJdovGcqsNLUk6kKlqaWyHHOnpror\nuYj2FVHL71WKYHjvvfdq1KhRGjJkiFq3bq358+ere/fuqlGjhkU7V1dX9ejRQ5MnT1ZwcHC5+khN\n/e1RFykpl29K3WWVknJZiYmFv9BtXUdJtQD44/huoRZqoZZboZbKUge1UAu13PxaSgqIFXYp6YwZ\nMzRz5kxJ+c8pDA0N1ZQpU9SlSxclJSVp7ty55rYmk8n8d0hIiE6ePMllpAAAAABwk9jsjGHHjh21\nc+dO8/SsWbMslvfv31/9+/cvct0jR46Y/+7Zs6fFdGBgoAIDA29usQAAAABgRyrF4DMAAAAAgIpD\nMAQAAAAAO0cwBAAAAAA7RzAEAAAAADtHMAQAAAAAO0cwBAAAAAA7RzAEAAAAADtHMAQAAAAAO0cw\nBAAAAAA7RzAEAAAAADtHMAQAAAAAO0cwBAAAAAA7RzAEAAAAADtHMAQAAAAAO0cwBAAAAAA7RzAE\nAAAAADtHMAQAAAAAO0cwBAAAAAA7RzAEAAAAADtHMAQAAAAAO1elogsAgD8iNzdX8fFxNu3Tx6eZ\nHB0dbdonAACANREMAfypxcfHacnXY1SrjotN+ktNzNT4Qcvk69vcJv0BAADYAsEQwJ9erTou8qrv\nVtFlAAAA/GlxjyEAAAAA2DmCIQAAAADYOS4lhSQG8AAAAADsGcEQkvIH8Dj+n6fVqLZt7tNKSL4i\nPbKQATwAAACASoBgCLNGtd3UrG71ii4DAAAAgI1xjyEAAAAA2DmCIQAAAADYOYIhAAAAANg5giEA\nAAAA2DmCIQAAAADYOYIhAAAAANg5giEAAAAA2DmCIQAAAADYOYIhAAAAANg5giEAAAAA2DmCIQAA\nAADYOYIhAAAAANg5giEAAAAA2DmCIQAAAADYOasHw8OHD2vYsGHy9/fX4MGDdeDAgSLbjRs3Tnfd\ndZfatWsnf39/tWvXzrzMz89P/v7+ysjIsFgnJydHAQEBCgoKsuprAAAAAIBbmVWDYXZ2tiZMmKCh\nQ4cqNjZWI0eO1IQJE5SZmVmo7ZEjR/T5559r79692rdvn/bu3Wux3NnZWZGRkRbzoqOjlZOTY82X\nAAAAAAC3PKsGw5iYGDk6OmrEiBFydHTUkCFDVLt2bW3dutWiXUpKilJSUnT77bcXu61+/fopPDzc\nYl5YWJj69u1rldoBAAAAwF5YNRjGxcXJ19fXYl7Tpk0VFxdnMe/w4cNyc3PTuHHj1LlzZz300EPa\nv3+/ebnJZFJwcLB2796ttLQ0SdKVK1cUGxurXr16WfMlAAAAAMAtz6rBMDMzUy4uLhbzXFxcdPXq\nVYt5WVlZ8vf317Rp07Rt2zaFhIRozJgxSk5ONrfx9PRUhw4dFBERIUnatGmTAgMD5eTkZM2XAAAA\nAAC3PKsGw6JCYGZmplxdXS3mBQUFacmSJfL19ZWTk5MefPBB1atXT7t27ZIkGYYhSRo4cKD5ctKw\nsDANGjTImuUDAAAAgF2oYs2NN2vWTJ9++qnFvFOnThUKdBs3blReXp4GDBhgnpedna1q1apZtOvT\np49mz56tQ4cOKSEhQe3bt1dUVFSZaqlVy1VVqjhKklJT3ZVyA6/nRnl6uqtOneqF5qemuivJhnWU\nVsu5SlILKr/c3FydPHnSpn36+vrK0dGx0PzUVHeb1iGV/O/IHuugFmqhFlvUkl5J6siyWR2l15Ja\naWo5U4n2S4YKD/JYUbUk6kKlqaUyHXMnF9G+Imr5PasGw06dOik7O1uffvqpRowYobVr1yolJUVd\nu3a1aJeRkaE33nhDLVq0UJMmTfTRRx8pKytLXbp0sWjn6uqqHj16aPLkyQoODi5XLampvz3qIiXl\n8o2/qBuQknJZiYmFv9BtXcefpZbc3FzFx8cVsYb1+Pg0KzJ0oGgnTx7XZ2vGyKuOS+mNb4KkxEw9\nNGSZfH2bF1pWmT67fLdQC7VQy61QS2Wpg1qohVpufi0lBUSrBsOqVatq2bJlmj59uubPn68mTZpo\n8eLFcnZ21owZM2QymTRz5kwNHjxYiYmJeuKJJ/Trr7+qdevWWrZsmZydnSXlDz5TICQkRBMnTtTC\nhQutWToqUHx8nHZ/NkH1vVxLb3wTnEvKkB5aXGToQPG86rjI+za3ii4DAAAAN4FVg6EktWjRQl98\n8UWh+bNmzbKYHjt2rMaOHVvkNo4cOWL+u2fPnhbTgYGBCgwMvDnFotKo7+WqJt62v4wHAAAAsEdW\nHXwGAAAAAFD5EQwBAAAAwM4RDAEAAADAzhEMAQAAAMDOEQwBAAAAwM4RDAEAAADAzhEMAQAAAMDO\nWf05hsCfWW5uruLj42zap49PMzk6OlbqWgAAAHBrIRgCJYiPj1PkinGq5+Vqk/5+ScpQ0Iil8vVt\nXmQtX68aqzpeLjapJTEpU4OGvVtkLQAAALi1EAyBUtTzclXDem4VXYYkqY6Xi+pXkloAAABw6+Ae\nQwAAAACwcwRDAAAAALBzBEMAAAAAsHMEQwAAAACwcwRDAAAAALBzBEMAAAAAsHMEQwAAAACwcwRD\nAAAAALBzBEMAAAAAsHMEQwAAAACwcwRDAAAAALBzBEMAAAAAsHMEQwAAAACwcwRDAAAAALBzBEMA\nAAAAsHMEQwAAAACwcwRDAAAAALBzBEMAAAAAsHMEQwAAAACwcwRDAAAAALBzBEMAAAAAsHMEQwAA\nAACwcwRDAAAAALBzBEMAAAAAsHMEQwAAAACwcwRDAAAAALBzBEMAAAAAsHMEQwAAAACwcwRDAAAA\nALBzBEMAAAAAsHMEQwAAAACwcwRDAAAAALBzBEMAAAAAsHNWD4aHDx/WsGHD5O/vr8GDB+vAgQNF\ntgsPD1fv3r3l7++v8ePHKzk52bzMz89P/v7+ysjIsFgnJydHAQEBCgoKsuprAAAAAIBbmVWDYXZ2\ntiZMmKChQ4cqNjZWI0eO1IQJE5SZmWnR7ujRo5o5c6YWLFigXbt2ycvLS1OmTLFo4+zsrMjISIt5\n0dHRysnJseZLAAAAAIBbnlWDYUxMjBwdHTVixAg5OjpqyJAhql27trZu3WrRruBsYZs2bVS1alU9\n//zzio6OVkpKirlNv379FB4ebrFeWFiY+vbta82XAAAAAAC3PKsGw7i4OPn6+lrMa9q0qeLi4kps\nV7NmTXl4eFi0Cw4O1u7du5WWliZJunLlimJjY9WrVy8rvgIAAAAAuPVZNRhmZmbKxcXFYp6Li4uu\nXr1a7naenp7q0KGDIiIiJEmbNm1SYGCgnJycrFQ9AAAAANiHKtbceHEh0NXV1WKes7Nzke3c3Nws\n5g0cOFBffvmlhg0bprCwME2YMEGXL1++odp+Tk68ofVupJ8mJSw/nZxSwtKb63RyinxKWJ6QfMVW\npSgh+Yqal7D8XFJGCUtvrnNJGWpYwvJfbFjLL0kZal3C8sSkzBKW3lyl9ZWUaLtaSusr1Ya1lNbX\n5Qu2qaW0fjIuptmkjrL0lXnBdt9zpfWVeTHJRpWU3lfmRdv8f6gsfWVcvGijSkrvK+PieRtVUtBX\nm2KXX7l41ma15PdV/BHDpQsJNqkjv59WxS5PunDaJnX81lfxRwsXEn+2WS0XEn9WG91e7PJzSbbb\nL+eSTqthCfslIcV2tSSknFZLtSx2+ekU2/0bOp1yVk3lXfzy5HO2qyX5nHxKeI9OJ1+wYS0XSswi\n1zMZhmFYq5Bt27YpNDRUmzZtMs8LCQnRM888o969e5vnvf7660pNTdWcOXMkSSkpKeratatiYmJU\no0YN+fn5KTw8XPXr11e3bt308ccf69lnn1VERISioqIUGhpaaGAaAAAAAEDZWPVS0k6dOik7O1uf\nfvqpcnJytHr1anPou969996riIgI7d27V1lZWZo/f766d++uGjVqWLRzdXVVjx49NHnyZAUHB1uz\ndAAAAACwG1YNhlWrVtWyZcsUFhamgIAAffbZZ1q8eLGcnZ01Y8YMzZw5U1L+cwpDQ0M1ZcoUdenS\nRUlJSZo7d655OyaTyfx3SEiITp48qUGDBlmzdAAAAACwG1a9lBQAAAAAUPlZ9YwhAAAAAKDyIxgC\nAAAAgJ0jGAIAAACAnSMYlsHhw4c1bNgw+fv7a/DgwTpw4EBFl6SDBw+qW7duFVpDbGyshg8frvbt\n26tv375asWJFhdWyfv16BQcHy9/fXyEhIdq8eXOF1VIgKSlJ99xzj7Zu3VphNXzwwQe688471a5d\nO/n7+6tdu3b6/vvvK6SWCxcuaPz48br77rsVGBioTz75pELqCAsLM++Lgv1yxx13aPr06TavZe/e\nvRoyZIjuvvtuDRgwQOHh4TavocDOnTs1ePBg3X333frrX/+qgwcP2ryG33+vXbp0SZMmTVL79u3V\nq1cvrV69usJqKZCSkqJevXrp1KlTFVbLhQsX9OSTTyogIEBdu3bVyy+/rGvXrlVILUePHtXIkSPN\n/67feecdm9RRVC0FDMPQqFGj9Nprr1VYLT/++KNatWpl8d377rvvVkgt165dU2hoqDp16qROnTpp\n2rRpFfJ5OX/+vMV3b7t27XTnnXeqf//+Nq1Dki5evKjx48erY8eO6tatmxYsWGD1GoqrJSEhQWPG\njFGHDh3Ur18/rV271uo1FHf8VhHfuaUdS6ampqp37946ceJEhdVSEd+5xdVi9e9cAyXKysoyunfv\nbnzxxRdGTk6OsXr1aqNz585GRkZGhdW0atUqo3379kanTp0qrIa0tDSjY8eOxrp16wzDMIxDhw4Z\nHTt2NHbs2GHzWk6dOmW0bdvW2L9/v2EYhrFjxw7jzjvvNFJTU21ey/XGjh1rtGrVyoiKiqqwGv7v\n//7P+PDDDyus/+s98MADxr/+9S8jNzfXOHHihNGxY0dj3759FV2WsWPHDqNbt27GhQsXbNpvbm6u\n0blzZyMiIsIwDMPYs2eP0bp1a+Ps2bM2rcMwDOPMmTNG27ZtjVWrVhm5ublGVFSU0bFjRyMpKclm\nNRT1vfbUU08ZL7zwgpGdnW0cOHDA6Nixo3HgwIEKqcUwDGP37t1Gv379DD8/PyMuLs7qdRRXy8iR\nI43Q0FAjOzvbSEpKMoYPH268+eabNq8lLy/P6Nmzp/HJJ58YhmEY586dM7p27Wps2bLF5rVcb9my\nZUarVq2MV1991ep1FFfLypUrjXHjxtmk/9JqmTdvnvHII48Yly5dMtLS0owRI0YYS5curZBarpeY\nmGh069bN+O6772xex1NPPWXMmzfPyMvLM3755RcjKCjIWLt2rVXrKKqW3NxcIyQkxJg6daqRlZVl\nnDp1yujZs6exdetWq9VQ0vGbrb9zSzuW3LNnjzFgwADDz8/POH78uNXqKK0WW3/nFlfLd999Z/Xv\nXM4YliImJkaOjo4aMWKEHB0dNWTIENWuXbvCzgItWbJEy5cv14QJEyqk/wLnzp1TYGCg+XmSrVq1\nUkBAgPbt22fzWnx8fLRjxw79P3v3HhBVnf9//MVVLipIXgr7JkolZWYogisqiCjIRb+ldvumXVWs\n1rLsp1Qqgpm1healLCt312zLzNogK5RS2TKQTN1QqxVsSdPEIUBAcYTfH36dr5OK1HpmkPN8/OOc\nc08UTMoAACAASURBVD7nnPfwGT8zrzmX6dWrl6xWqw4dOqTWrVvLw8PD4bWc8tZbb8nX11eXXnqp\n02qQpF27dql79+5OrUGStm/frkOHDunRRx+Vq6urgoOD9fbbb6tr165Orau6ulrTp09XWlqaOnbs\n6NB9V1ZWqry83Pato4uLizw8POTm5ubQOiRp06ZN6t69u0aPHi1XV1dFRUWpV69e+vjjjx2y/7ON\nazU1NcrNzdXkyZPl4eGh66+/XsnJyYZ/m36uMXbLli165JFHHDr2nq2W48ePy9fXV5MmTZKHh4cu\nueQSJScnGz72nq0WFxcXrV27VnfccYekk0dTGxoa5Ofn5/BaTtm9e7fee+89xcbGGlrD+WrZuXOn\nrrnmGofU0FgtVqtVq1at0syZM9WmTRu1bdtWixYtUnJyssNr+bWZM2cqISFBkZGRDq+jpKREVqtV\nVqtVDQ0NcnNzk5eXl2F1nKuWkpIS7dmzRzNmzJCnp6eCgoJ0++23G3qk7lyf37Zu3apPP/3UoWNu\nY58lv/rqKz388MNKSUkxbP9NqWXr1q0OH3PPVcv27dsNH3MJhudRXFys4OBgu3ldu3ZVcXGxU+oZ\nPXq03n//fV133XVO2f8pISEheuaZZ2zTFRUVKiwsdPgb4Sne3t768ccf1atXL02fPl1TpkyRr6+v\nU2opKSnR8uXLlZaWpgYn/hrM0aNHVVJSor/+9a8aMGCAEhMT9e677zqllqKiIl155ZV69tlnNWDA\nAMXHx2vbtm2Gf4A8n1dffVXdu3dXTEyMw/ft7++v2267TY888oh69OihsWPHaubMmerUqZPDa6mv\nrz/jQ5Grq6v27t3rkP2fbVzbu3evPDw81LlzZ9s8R4y95xpju3fvrtzcXCUlJTns//XZavHw8NDS\npUt1ySWX2OZ99tlnCgkJcXgtkmyvm9jYWI0ePVr9+/dX7969nVJLXV2dpk+frjlz5sjHx8fQGs5X\ny65du/TVV19pyJAhiomJ0TPPPGP4qWdnq+WHH35QfX29tm3bpri4OEVFRWn58uWGfxF2vs8qmzdv\n1rZt2/TQQw85pY777rtPq1atUmhoqAYPHqzevXsrLi7O4bXU19fLzc3N7otsFxcX/fDDD4bVca7P\nb5Lk7u7u0DH3XLWEhITo6quv1qeffqoRI0Y4ZMw9Vy3XXnutw8fcxv4uRo+5BMPzqK2tlbe3t908\nb29vHT161Cn1tG/f3in7bUxVVZVSUlLUs2dPDR482Gl1BAYGaseOHXr99df19NNPKz8/3+E1nDhx\nQtOmTdOMGTPUtm1bh+//dGVlZerTp49uv/12bdiwQbNnz9a8efOUl5fn8FoqKiqUn5+vgIAAbdiw\nQU8//bQyMjKcdr2jdPKI1MqVK/Xggw86Zf8NDQ3y8vLSokWLtH37dr300kt66qmn9O233zq8lgED\nBmj79u3KycmR1WrVpk2btHnzZh07dswh+z/buFZbW6tWrVrZzfPy8jJ87D3XGNu2bVt5enoauu+m\n1nK6OXPmqKSkRBMmTHBqLWvXrlVOTo6++eYbLV682Cm1ZGZmatCgQQoNDTV0/02pJSAgQDExMfrw\nww/117/+Vfn5+Vq0aJHDa/nll19UV1enDRs26N1339WqVav0+eefa9myZQ6v5XTLli3TPffcc8bn\nK0fV0dDQoJSUFG3dulXZ2dkqLCzUqlWrHF5Lt27d1LlzZz3//PM6duyYSkpKtGrVKoeNvVVVVZo0\naZJ69uypiIgIp4y5p9dy6rNkTEyM2rRp4/Ax92y1/PpzraPG3LPVcvqX2EaNuQTD8zhbCKytrXXY\nt5HNXWlpqW677TYFBAQY/qZ3Pq6urnJzc1O/fv0UFxfnlBvQLFmyRNdcc40GDBjg8H3/2uWXX64V\nK1Zo4MCBcnd3V1hYmEaOHOmUv4unp6f8/f01fvx4ubu7KzQ0VMOGDVNubq7Dazll/fr16ty5s66/\n/nqn7D8nJ0f//Oc/NXToULm7uysqKkrR0dEOufHAr3Xp0kULFizQkiVLNHDgQH3wwQcaPny4U7/c\n8Pb2Vl1dnd28o0ePMvb+r2PHjmny5Mn6/PPP9cYbbyggIMCp9Xh6euq//uu/dN9992ndunUO3//m\nzZv15ZdfavLkyQ7f99m8+OKLuuuuu+Tl5aXLL79cKSkpTvm7eHp6qqGhQQ8//LBat26tTp066e67\n73bqDdoOHDigLVu2aPTo0U7Z/88//6y0tDSNHz9enp6eCg4O1oQJE5xyAz03Nze9+OKL2rVrl6Ki\nojRjxgyNHDnSIWPvqc9v7dq106JFi+Tj4+O0Mbc5fZY8Vy3OGHMb+7sYNeYSDM+jW7duZ9x9rqSk\nRFdeeaWTKmo+ioqKdMstt2jgwIFasmSJ077Z2bhxo+6++267ecePH3fKh9qPPvpIa9euVXh4uMLD\nw/XTTz9pypQphn87ezY7d+484y54x44dO+MbQUfo2rWr7VqOU+rr6516qu1nn32m4cOHO23/P/30\n0xlvwu7u7nJ3d3d4LdXV1brsssv097//XZs3b9Zzzz2nkpISXXvttQ6v5ZQuXbro+PHjOnDggG1e\nSUnJGaf2m1FFRYXuuOMOVVVVadWqVQoMDHRKHRaLRbGxsaqsrLTNq6urc9rYW1paqv79+ys8PFxZ\nWVlauXKlw65POl1lZaWeeeYZ1dTU2OYdPXrUKWNvUFCQXF1d7caaX4/FjvbZZ58pPDxc/v7+Ttl/\nWVmZ7frCU1xdXZ0y9jY0NKi6ulqvvfaavvzyS73xxhuqra01/LKcs31+c9aY21w+SzZWizPG3LPV\n4ogxl2B4Hv369VNdXZ1Wrlwpq9Wq1atXy2KxNIsjQs5UVlam8ePH65577tG0adOcWkuPHj1UVFSk\nDz74QA0NDdq4caM2bdqkpKQkh9fy0UcfacuWLSooKFBBQYEuu+wyzZ8/X+PHj3d4LT4+PlqyZIly\ncnLU0NCgzZs3a+3atbrpppscXktkZKS8vb21ePFinThxQlu3btX69eudGsy2b9+uG264wWn779+/\nv3bt2qX33ntPklRQUOC0v8kvv/yiW265RTt37rSNdwcOHHDKtZen+Pr6KiYmRs8//7yOHj2qHTt2\nKDs72/CbZlwMHnzwQXXo0EGvvvqq2rRp47Q6AgIC1L59e82fP1/Hjx/Xnj179NprrznlSFB6erq+\n+uor29ibnJys//mf/9HSpUsdXkubNm20fv16LVq0SFarVT/88INefvlljRo1yim1DBkyRJmZmaqq\nqtLBgwf1l7/8xXZTC2fYvn27Q0/3/bUrr7xSnTp10rx581RXV6cff/xRy5cvV2JiosNrcXFx0SOP\nPKK3335bDQ0NKigo0DvvvKNbb73VsH2e6/ObM8bc5vRZ8ly1NDQ0OHzMPVctjhhzHf/1yEXG09NT\ny5Yt08yZM5WZmakuXbropZdeMvzuVc3du+++q/Lycr344otasmSJpJMD3Lhx4/Twww87tJb27dvr\npZde0ty5c5Wenq6goCC9+OKLTr/jpXTyb+IsQUFBeuGFF5SZmalp06bp0ksv1bx58wy/ScXZtGrV\nSitWrNDs2bPVv39/tW7dWjNmzHDaaZz19fU6cOCAOnTo4JT9S9LVV1+thQsXasGCBXrqqad02WWX\n6ZlnnnHKUbrOnTsrPT1df/zjH1VRUaFrr71Wr7/+utPHuYyMDM2aNUtRUVHy9fXVtGnTnPaaOZ0z\n/19//fXXKiwsVKtWrRQWFmarpUePHk75bdAXXnhBs2bNUmRkpPz9/XX33Xdr5MiRDq+jOXFxcdHS\npUs1Z84c9evXT15eXrr11ls1duxYp9Qzb948zZs3TwkJCTp+/LhuvPHGM86ycaR9+/Y5NRh6enrq\nlVde0dy5czVw4ED5+vrq5ptv1rhx45xSz/z58zVr1iz96U9/UmBgoJ566ilDjxg29vltzpw5mjlz\npsPG3KZ+lnTEmHuuWq677jqHj7mN/V2MHnNdGpx5PgEAAAAAwOk4lRQAAAAATI5gCAAAAAAmRzAE\nAAAAAJMjGAIAAACAyREMAQAAAMDkCIYAAAAAYHIEQwAAAAAwOYIhAAC/Q0hIiP71r3+dMb9fv37a\nsmWLEyoCAOD3IxgCAPA7uLi4OLsEAAAuGIIhAAC/Q0NDw3nb/Pvf/1ZKSorCw8M1dOhQvfrqq7Zl\nY8eO1cqVK23TK1eu1NixYyVJixcvVkpKihITExUdHa3q6uoL/wQAADiNu7MLAADgYnXrrbfK1fX/\nvmNtaGiwhbjjx4/r7rvvVkJCghYvXqx///vfmjhxotq0aaNbbrnlrNs7/Shkfn6+Vq9erU6dOsnX\n19fYJwIAMD2CIQAAv9Pbb7+t4OBgu3n9+vWTJBUWFurIkSOaMmWKXF1d1a1bN91333167733zhkM\nT3fNNdecsW0AAIzCqaQAAPxOjZ1OarFY1LFjR7sjioGBgTpw4ECTtt2+ffv/uD4AAJqKYAgAgAEu\nu+wy/fzzz6qvr7fNKy0t1SWXXCJJcnNz0/Hjx23LysvL7dbn5jYAAEciGAIAYIDrr79e7du314IF\nC1RXV6c9e/bo9ddf14gRIyRJQUFBysvLU11dnUpLS5WVleXkigEAZkYwBADgdzjXEb1T893d3bV0\n6VLt3r1bAwYM0D333KObb75Zd955pyRpwoQJslqtioyM1EMPPaQbb7zRYbUDAPBrLg1Nud82AAAA\nAKDF4oghAAAAAJgcwRAAAAAATI5gCAAAAAAmRzAEAAAAAJMjGAIAAACAyREMAQAAAMDkCIYAAAAA\nYHIEQwAAAAAwOYIhAAAAAJgcwRAAAAAATI5gCAAAAAAmRzAEAAAAAJMzPBgePHhQKSkp6tOnj6Kj\no7VixYqztsvOzlZsbKxCQ0OVkpKiw4cP25aFhIQoNDRUNTU1dutYrVZFRERoyJAhhj4HAAAAAGjJ\nDA+G999/v6688kpt2bJFr732mhYvXqxt27bZtdm9e7fS0tI0f/585efnq3379kpNTbVr4+Xlpdzc\nXLt5eXl5slqtRj8FAAAAAGjRDA2G27dv16FDh/Too4/K1dVVwcHBevvtt9W1a1e7dqeOFvbs2VOe\nnp6aOnWq8vLyZLFYbG3i4uKUnZ1tt15WVpaGDRtm5FMAAAAAgBbP0GBYVFSkK6+8Us8++6wGDBig\n+Ph4bdu2TX5+fnbtiouLFRwcbJv29/eXn5+fiouLbfMSEhJUUFCgiooKSVJ1dbUKCwsVExNj5FMA\nAAAAgBbP0GBYUVGh/Px8BQQEaMOGDXr66aeVkZGhr776yq5dbW2tvL297eZ5e3vr6NGjtumAgAD1\n7dtXOTk5kqR169YpOjpaHh4eRj4FAAAAAGjxDA2Gnp6e8vf31/jx4+Xu7q7Q0FANGzbsjGsFvby8\n7EKgdDIs+vr62s1LTEy0nU6alZWlESNGGFk+AAAAAJiCocGwa9euslqtamhosM2rr6+3m5ak4OBg\nlZSU2KYtFosqKyvtTi+VpKFDh+qbb75RUVGRSktLFRYW1uRarNYTv/NZAAAAAEDL5m7kxiMjI+Xt\n7a3Fixfr/vvv1/bt27V+/XotX77crl1SUpLGjh2rUaNGqUePHsrMzNSgQYPUtm1bu3Y+Pj6KiorS\ntGnTlJCQ8JtqKS+vOX8jAAAAAGihOnRoc85lhh4xbNWqlVasWKHt27erf//+euyxxzRjxgxdf/31\nmjVrltLS0iSd/J3CjIwMpaamKjIyUmVlZZo7d65tOy4uLrbHycnJ2rNnD6eRAgAAAMAF4tLw6/M6\nW6hDh6qcXQIAAAAAOI3TjhgCAAAAAJo/giEAAAAAmBzBEAAAAABMjmAIAAAAACZHMAQAAAAAkyMY\nAgAAAIDJEQwBAAAAwOQIhgAAAABgcgRDAAAAADA5giEAAAAAmBzBEAAAAABMjmAIAAAAACZHMAQA\nAAAAkyMYAgAAAIDJEQwBAAAAwOQIhgAAAABgcgRDAAAAADA5giEAAAAAmBzBEAAAAABMjmAIAAAA\nACZHMAQAAAAAkyMYAgAAAIDJEQwBAAAAwOQIhgAAAABgcgRDAAAAADA5giEAAAAAmBzBEAAAAABM\njmAIAAAAACZHMAQAAAAAkyMYAgAAAIDJEQwBAAAAwOQIhgAAAABgcgRDAAAAADA5d2cXAAAAgJbr\nxIkT2ru32NllmFpQUDe5ubk5uww0c4YHw9dff12ZmZny9PRUQ0ODXFxctGzZMvXp08euXXZ2thYs\nWKDDhw8rIiJCTz31lC655BJJUkhIiLy9vfX555/Lx8fHto7ValVkZKRat26t3Nxco58KAAAAfqO9\ne4u1JWOGAtu0cXYpprS/qkqakaHg4KucXQqaOcOD4c6dOzV16lTddddd52yze/dupaWlafny5ere\nvbvS09OVmpqqV155xdbGy8tLubm5Sk5Ots3Ly8uT1Wo1snwAAAD8hwLbtFEXf39nlwGgEYZfY7hr\n1y5179690TbZ2dmKjY1Vz5495enpqalTpyovL08Wi8XWJi4uTtnZ2XbrZWVladiwYYbUDQAAAABm\nYWgwPHr0qEpKSvTXv/5VAwYMUGJiot59990z2hUXFys4ONg27e/vLz8/PxUX/9/56AkJCSooKFBF\nRYUkqbq6WoWFhYqJiTHyKQAAAABAi2doMCwrK1OfPn10++23a8OGDZo9e7bmzZunvLw8u3a1tbXy\n9va2m+ft7a2jR4/apgMCAtS3b1/l5ORIktatW6fo6Gh5eHgY+RQAAAAAoMUzNBhefvnlWrFihQYO\nHCh3d3eFhYVp5MiRWr9+vV07Ly8vuxAonQyLvr6+dvMSExNtp5NmZWVpxIgRRpYPAAAAAKZg6M1n\ndu7cqX/84x+aMGGCbd6xY8fOODoYHByskpIS27TFYlFlZaXd6aWSNHToUKWnp6uoqEilpaUKCwvT\nhg0bmlRLu3Y+cnfnNr0AAACOVF7eWvucXYTJBQS0VocO3BUWjTM0GPr4+GjJkiUKCgrS0KFD9eWX\nX2rt2rVauXKlXbukpCSNHTtWo0aNUo8ePZSZmalBgwapbdu2Z2wvKipK06ZNU0JCwm+qpby85j9+\nPgAAAPhtLJYjzi7B9CyWIzp0qMrZZaAZaOwLAkNPJQ0KCtILL7ygxYsXq3fv3kpPT9e8efMUEhKi\nWbNmKS0tTdLJ3ynMyMhQamqqIiMjVVZWprlz59q24+LiYnucnJysPXv2cBopAAAAAFwgLg0NDQ3O\nLsIR+JYEAADA8fbs+V77FjzL7xg6yQ+//KLOD/8/fuAekpx4xBAAAAAA0PwRDAEAAADA5AiGAAAA\nAGByht6VFAAA4HxOnDihvXuLnV2GqQUFdZObGz/rBZgZwRAAADjV3r3F2pLxlgLbdHB2Kaa0v+qQ\nNONWbk4CmBzBEAAAOF1gmw7q4n+Zs8sAANPiGkMAAAAAMDmCIQAAAACYHMEQAAAAAEyOYAgAAAAA\nJkcwBAAAAACT466kAICLAr9151z8zh0AtGwEQwDARWHv3mLlZnyuTm0CnV2K6Rys2q8hM8Tv3AFA\nC0YwBABcNDq1CdTl/l2cXQYAAC0O1xgCAAAAgMkRDAEAAADA5AiGAAAAAGByBEMAAAAAMDmCIQAA\nAACYHMEQAAAAAEyOYAgAAAAAJkcwBAAAAACTIxgCAAAAgMkRDAEAAADA5NydXQAAAACAi8+JEye0\nd2+xs8swtaCgbnJzc7sg2yIYAmgxeINyvgv5BgUAaN727i3WljUzFNihjbNLMaX9h6qkmzIUHHzV\nBdkewRBAi7F3b7FeyPhc/m0CnV2KKf1StV8PzdAFe4MCADR/gR3aqMtl/s4uAxcAwRBAi+LfJlCX\n+HdxdhkAAAAXFW4+AwAAAAAmRzAEAAAAAJMjGAIAAACAyREMAQAAAMDkCIYAAAAAYHIEQwAAAAAw\nOYcEw7KyMvXv318bN2486/Ls7GzFxsYqNDRUKSkpOnz4sG1ZSEiIQkNDVVNTY7eO1WpVRESEhgwZ\nYmjtAAAAANDSOSQYPvHEE6qoqDjrst27dystLU3z589Xfn6+2rdvr9TUVLs2Xl5eys3NtZuXl5cn\nq9VqWM0AAAAAYBaGB8O33npLvr6+uvTSS8+6/NTRwp49e8rT01NTp05VXl6eLBaLrU1cXJyys7Pt\n1svKytKwYcMMrR0AAAAAzMDQYFhSUqLly5crLS1NDQ0NZ21TXFys4OBg27S/v7/8/PxUXFxsm5eQ\nkKCCggLbUcfq6moVFhYqJibGyPIBAAAAwBTcjdrwiRMnNG3aNM2YMUNt27Y9Z7va2lp5e3vbzfP2\n9tbRo0dt0wEBAerbt69ycnI0ZswYrVu3TtHR0fLw8DCqfLRgJ06c0N69xedvCEMEBXWTm5ubs8sA\nAADAaQwLhkuWLNE111yjAQMGNNrOy8vLLgRKJ8Oir6+v3bzExEStWbNGY8aMUVZWliZNmqQjR45c\n8LrR8u3dW6yMP3+uNgGBzi7FdKos+zXjLik4+CpnlwIAAIDTGBYMP/roI5WVlemjjz6SJFVVVWnK\nlCmaNGmSxo8fb2sXHByskpIS27TFYlFlZaXd6aWSNHToUKWnp6uoqEilpaUKCwvThg0bmlxPu3Y+\ncnfnKAWk8vLWahMQKP+OXZxdiikFBLRWhw5tDNl2eXlrQ7aLpqN/Wy6j+3afIVtGU9G/LZtR/Uvf\nOt+F7FtDg+HpYmJiNGvWLEVFRdnNT0pK0tixYzVq1Cj16NFDmZmZGjRo0Bmnn/r4+CgqKkrTpk1T\nQkLCb66nvLzm/I1gChYLR5qdyWI5okOHqgzbNpyL/m256NuWjf5t2YzqX/rW+X5r3zYWIh32A/cu\nLi62x7NmzVJaWpqkk79TmJGRodTUVEVGRqqsrExz584963rJycnas2ePRowY4aiyAQAAAKDFM+yI\n4a+d/juEs2fPtlsWHx+v+Pj4s663a9cu2+PBgwfbTUdHRys6OvrCFgoAAAAAJuOwI4YAAAAAgOaJ\nYAgAAAAAJkcwBAAAAACTIxgCAAAAgMkRDAEAAADA5AiGAAAAAGByBEMAAAAAMDmH/Y7hxeTEiRPa\nu7fY2WWYWlBQN7m5uTm7DAAAAMAUCIZnsXdvsTK2fK42gYHOLsWUqvbv1wxJwcFXObsUAAAAwBQI\nhufQJjBQ/l26OLsMAAAAADAc1xgCAAAAgMkRDAEAAADA5AiGAAAAAGByBEMAAAAAMDmCIQAAAACY\nHMEQAAAAAEyOYAgAAAAAJkcwBAAAAACTIxgCAAAAgMkRDAEAAADA5AiGAAAAAGByBEMAAAAAMDmC\nIQAAAACYHMEQAAAAAEyOYAgAAAAAJkcwBAAAAACTIxgCAAAAgMkRDAEAAADA5JoUDDMyMs6YN23a\ntAteDAAAAADA8dwbW/jEE0+otLRU33zzjb7//nvbfKvVqqqqKsOLAwAAAAAYr9FgOGnSJO3bt09P\nPfWUHnzwQdt8Nzc3BQcHG14cAAAAAMB4jQbDyy+/XJdffrk++OADHTlyRFVVVWpoaJAk1dTUyN/f\n3yFFAgAAAACM02gwPOXll1/Wyy+/bBcEXVxclJuba1hhAAAAAADHaFIwfOedd7R+/XoFBAQYXQ8A\nAAAAwMGadFfSyy67TH5+fr9rB2vXrlVCQoJCQ0OVnJys9evXn7Vddna2YmNjFRoaqpSUFB0+fNi2\nLCQkRKGhoaqpqbFbx2q1KiIiQkOGDPldtQEAAAAAmnjEMCgoSLfffrsiIiLk6elpm3/6DWnOZu/e\nvXriiSf05z//Wb169dLmzZs1YcIE5eXl2Z2Wunv3bqWlpWn58uXq3r270tPTlZqaqldeecXWxsvL\nS7m5uUpOTrbNy8vLk9VqbfKTBQAAAACcqUlHDDt16qSBAwfahcKmCAoK0hdffKFevXrJarXq0KFD\nat26tTw8POzanTpa2LNnT3l6emrq1KnKy8uTxWKxtYmLi1N2drbdellZWRo2bNhvqgkAAAAAYK9J\nRwzPd2SwMd7e3vrxxx8VFxenhoYGpaWlydfX165NcXGxQkNDbdP+/v7y8/NTcXGx7brGhIQETZw4\nURUVFfLz81N1dbUKCws1Y8YMFRQU/O76AAAAAMDsmhQMQ0JC5OLiYjevY8eO2rhxY5N2EhgYqB07\ndmjLli2aNGmSunTpooiICNvy2tpaeXt7263j7e2to0eP2qYDAgLUt29f5eTkaMyYMVq3bp2io6PP\nOPoIAAAAAPhtmhQMd+/ebXt8/PhxrV+/Xtu2bWvyTlxdT56x2q9fP8XFxWn9+vV2wdDLy8suBEon\nw+KvjywmJiZqzZo1GjNmjLKysjRp0iQdOXKkSTW0a+cjd3e3JrUtL28t7WtSUxgkIKC1OnRoY8i2\ny8tbG7JdNA1927LRvy2X0X3L265z0b8tm1H9S98634Xs2yYFw9N5eHho+PDhWrp06Xnbbty4UX/+\n85+1fPly27zjx4+rbdu2du2Cg4NVUlJim7ZYLKqsrFRwcLBdu6FDhyo9PV1FRUUqLS1VWFiYNmzY\n0KS6y8trzt/Itv+mhU0Yx2I5okOHqgzbNpyHvm3Z6N+Wi75t2ejfls2o/qVvne+39m1jIbJJwfD9\n99+3PW5oaND333/fpFM4e/TooaKiIn3wwQdKTk7Wpk2btGnTpjOuWUxKStLYsWM1atQo9ejRQ5mZ\nmRo0aNAZAdLHx0dRUVGaNm2aEhISmlI6AAAAAOA8mhQM8/Pz7abbtWun+fPnn3e99u3b66WXXtLc\nuXOVnp6uoKAgvfjii+ratatmzZolFxcXpaWlKSQkRBkZGUpNTdXhw4cVFhamuXPn2rZz+vWNycnJ\nuv/++7Vw4cKmPkcAAAAAQCOaFAyffvppHT9+XCUlJTpx4oSuuuoqubs37SzUPn366N133z1j/uzZ\ns+2m4+PjFR8ff9Zt7Nq1y/Z48ODBdtPR0dGKjo5uUi0AAAAAgDM1Kd198803mjx5svz9/VVfFrN4\nZQAAIABJREFUX6+ysjItWbJEvXr1Mro+AAAAAIDBmhQM58yZo/nz59uC4LZt25SRkaHVq1cbWhwA\nAAAAwHiuTWlUU1Njd3Twhhtu0LFjxwwrCgAAAADgOE0Khn5+flq/fr1tev369fL39zesKAAAAACA\n4zTpVNKMjAxNnDhRTzzxhG3eW2+9ZVhRAAAAAADHadIRw02bNsnb21ufffaZ/vKXvyggIEAFBQVG\n1wYAAAAAcIAmBcNVq1bpb3/7m3x8fBQSEqI1a9bojTfeMLo2AAAAAIADNCkYHj9+XB4eHrbp0x8D\nAAAAAC5uTbrGMDY2VnfeeaeGDx8uScrJydGQIUMMLQwAAAAA4BhNCoaPPfaYPv74Y23ZskXu7u4a\nN26cYmNjja4NAAAAAOAATQqGkhQfH6/4+HgjawEAAAAAOEGTrjEEAAAAALRcBEMAAAAAMDmCIQAA\nAACYHMEQAAAAAEyOYAgAAAAAJkcwBAAAAACTIxgCAAAAgMkRDAEAAADA5AiGAAAAAGByBEMAAAAA\nMDmCIQAAAACYHMEQAAAAAEyOYAgAAAAAJkcwBAAAAACTIxgCAAAAgMkRDAEAAADA5AiGAAAAAGBy\nBEMAAAAAMDmCIQAAAACYHMEQAAAAAEyOYAgAAAAAJkcwBAAAAACTMzwYFhYW6uabb1ZYWJiGDRum\nt99++6ztsrOzFRsbq9DQUKWkpOjw4cO2ZSEhIQoNDVVNTY3dOlarVRERERoyZIihzwEAAAAAWjJD\ng2FlZaUeeOAB3XXXXSosLNSCBQuUmZmpzZs327XbvXu30tLSNH/+fOXn56t9+/ZKTU21a+Pl5aXc\n3Fy7eXl5ebJarUY+BQAAAABo8QwNhvv371d0dLQSEhIkSddee60iIiL09ddf27U7dbSwZ8+e8vT0\n1NSpU5WXlyeLxWJrExcXp+zsbLv1srKyNGzYMCOfAgAAAAC0eIYGw5CQED3zzDO26YqKChUWFuqa\na66xa1dcXKzg4GDbtL+/v/z8/FRcXGybl5CQoIKCAlVUVEiSqqurVVhYqJiYGCOfAgAAAAC0eA67\n+UxVVZVSUlLUs2dPDR482G5ZbW2tvL297eZ5e3vr6NGjtumAgAD17dtXOTk5kqR169YpOjpaHh4e\nxhcPAAAAAC2YQ4JhaWmpbrvtNgUEBGjRokVnLPfy8rILgdLJsOjr62s3LzEx0XY6aVZWlkaMGGFc\n0QAAAABgEu5G76CoqEjjx4/XyJEjNW3atLO2CQ4OVklJiW3aYrGosrLS7vRSSRo6dKjS09NVVFSk\n0tJShYWFacOGDU2qo107H7m7uzWpbXl5a2lfk5rCIAEBrdWhQxtDtl1e3tqQ7aJp6NuWjf5tuYzu\nW952nYv+bdmM6l/61vkuZN8aGgzLyso0fvx43XPPPbrvvvvO2S4pKUljx47VqFGj1KNHD2VmZmrQ\noEFq27atXTsfHx9FRUVp2rRpthvaNFV5ec35G/0vi+XIb9o2LjyL5YgOHaoybNtwHvq2ZaN/Wy76\ntmWjf1s2o/qXvnW+39q3jYVIQ4Phu+++q/Lycr344otasmSJJMnFxUXjxo1TeXm5XFxclJaWppCQ\nEGVkZCg1NVWHDx9WWFiY5s6da9uOi4uL7XFycrLuv/9+LVy40MjSAQAAAMA0DA2GEydO1MSJE5vU\nNj4+XvHx8WddtmvXLtvjwYMH201HR0crOjr6P6oTAAAAAMzMYXclBQAAAAA0TwRDAAAAADA5giEA\nAAAAmBzBEAAAAABMjmAIAAAAACZHMAQAAAAAkyMYAgAAAIDJEQwBAAAAwOQIhgAAAABgcgRDAAAA\nADA5giEAAAAAmBzBEAAAAABMjmAIAAAAACZHMAQAAAAAkyMYAgAAAIDJEQwBAAAAwOQIhgAAAABg\ncgRDAAAAADA5giEAAAAAmBzBEAAAAABMjmAIAAAAACZHMAQAAAAAkyMYAgAAAIDJEQwBAAAAwOQI\nhgAAAABgcgRDAAAAADA5giEAAAAAmBzBEAAAAABMjmAIAAAAACZHMAQAAAAAkyMYAgAAAIDJEQwB\nAAAAwOQIhgAAAABgcgRDAAAAADA5hwXDHTt2aODAgedcnp2drdjYWIWGhiolJUWHDx+2LQsJCVFo\naKhqamrs1rFarYqIiNCQIUMMqxsAAAAAWjqHBMPVq1fr3nvvldVqPevy3bt3Ky0tTfPnz1d+fr7a\nt2+v1NRUuzZeXl7Kzc21m5eXl3fObQIAAAAAmsbwYLh06VK98cYbmjRp0jnbnDpa2LNnT3l6emrq\n1KnKy8uTxWKxtYmLi1N2drbdellZWRo2bJhhtQMAAACAGRgeDEePHq33339f11133TnbFBcXKzg4\n2Dbt7+8vPz8/FRcX2+YlJCSooKBAFRUVkqTq6moVFhYqJibGuOIBAAAAwAQMD4bt27c/b5va2lp5\ne3vbzfP29tbRo0dt0wEBAerbt69ycnIkSevWrVN0dLQ8PDwubMEAAAAAYDLN4q6kXl5ediFQOhkW\nfX197eYlJibaTifNysrSiBEjHFYjAAAAALRU7s4uQJKCg4NVUlJim7ZYLKqsrLQ7vVSShg4dqvT0\ndBUVFam0tFRhYWHasGFDk/bRrp2P3N3dmtS2vLy1tK/J5cMAAQGt1aFDG0O2XV7e2pDtomno25aN\n/m25jO5b3nadi/5t2YzqX/rW+S5k3zaLYJiUlKSxY8dq1KhR6tGjhzIzMzVo0CC1bdvWrp2Pj4+i\noqI0bdo0JSQk/KZ9lJfXnL/R/7JYjvymbePCs1iO6NChKsO2Deehb1s2+rflom9bNvq3ZTOqf+lb\n5/utfdtYiHTaqaSzZs1SWlqapJO/U5iRkaHU1FRFRkaqrKxMc+fOtbV1cXGxPU5OTtaePXs4jRQA\nAAAALhCHHTEMDw/X5s2bbdOzZ8+2Wx4fH6/4+Pizrrtr1y7b48GDB9tNR0dHKzo6+sIWCwAAAAAm\n0ixuPgMAAAAAcB6CIQAAAACYHMEQAAAAAEyOYAgAAAAAJkcwBAAAAACTIxgCAAAAgMkRDAEAAADA\n5AiGAAAAAGByBEMAAAAAMDmCIQAAAACYHMEQAAAAAEyOYAgAAAAAJkcwBAAAAACTIxgCAAAAgMkR\nDAEAAADA5AiGAAAAAGByBEMAAAAAMDmCIQAAAACYHMEQAAAAAEyOYAgAAAAAJkcwBAAAAACTIxgC\nAAAAgMkRDAEAAADA5AiGAAAAAGByBEMAAAAAMDmCIQAAAACYHMEQAAAAAEyOYAgAAAAAJkcwBAAA\nAACTIxgCAAAAgMkRDAEAAADA5AiGAAAAAGByBEMAAAAAMDmCIQAAAACYnOHBcOfOnRozZoxCQ0N1\n4403avv27Wdtl52drdjYWIWGhiolJUWHDx+2LQsJCVFoaKhqamrs1rFarYqIiNCQIUMMfQ4AAAAA\n0JIZGgzr6uo0adIkjR49WoWFhbrjjjs0adIk1dbW2rXbvXu30tLSNH/+fOXn56t9+/ZKTU21a+Pl\n5aXc3Fy7eXl5ebJarUY+BQAAAABo8QwNhl9++aXc3Nx0yy23yM3NTaNGjdIll1yijRs32rU7dbSw\nZ8+e8vT01NSpU5WXlyeLxWJrExcXp+zsbLv1srKyNGzYMCOfAgAAAAC0eIYGw+LiYgUHB9vN69q1\nq4qLixtt5+/vLz8/P7t2CQkJKigoUEVFhSSpurpahYWFiomJMfAZAAAAAEDLZ2gwrK2tlbe3t908\nb29vHT169De3CwgIUN++fZWTkyNJWrdunaKjo+Xh4WFQ9QAAAABgDu5GbvxcIdDHx8dunpeX11nb\n+fr62s1LTEzUmjVrNGbMGGVlZWnSpEk6cuSIIbVX7d9vyHZxflX790uduxq7Dwv96wwn/+7G9u0v\nVfSts5z82xvbvwfpX6c4WLVf1xnct/urDhm6fZzb/qpD6mz4PqoM3gPOZX9VlaH9u/8Qfess+w9d\n2L41NBh269ZNK1eutJtXUlKiESNG2M0LDg5WSUmJbdpisaiysvKM01CHDh2q9PR0FRUVqbS0VGFh\nYdqwYUOTaunQoU2T6+7Qobc+7Ne7ye1xcaF/W64OHXqr34f0bUtF/7Zc9G3LdrJ/P3R2GTBAhw69\n1a8ffdtSGHoqab9+/VRXV6eVK1fKarVq9erVslgsGjBggF27pKQk5eTkaOvWrTp27JgyMzM1aNAg\ntW3b1q6dj4+PoqKiNG3aNCUkJBhZOgAAAACYhqHB0NPTU8uWLVNWVpYiIiL05ptv6qWXXpKXl5dm\nzZqltLQ0SSd/pzAjI0OpqamKjIxUWVmZ5s6da9uOi4uL7XFycrL27NlzxlFHAAAAAMDv49LQ0NDg\n7CIAAAAAAM5j6BFDAAAAAEDzRzAEAAAAAJMjGAIAAMCUKioqDPvpM+BiQzB0spCQEIWGhqqmpsZu\nvtVqVUREhIYMGXLB9/nMM88oNTX1gm8XJ40fP16hoaHq3bu3evTooeuuu842feqGS0br16+ftmzZ\n4pB9tRQTJ07Uc889Zzfv3nvvVY8ePew+NBQWFio0NFTHjx9v8rY3bNigmJiYC1ZrU/AaaLqz/Z/t\n3bu3evfureHDh6tfv34Or4lx2rFiYmLUq1cvW7+fej2sW7fujLbjx4/XO++8c9bt0G/OsXfvXt1/\n//0KDw9Xnz599N///d9avXp1k9aNi4vTgQMHLkgd9L9j/Cf9/VtMnjxZixcvvuDbbc4M/R1DNI2X\nl5dyc3OVnJxsm5eXlyer1erEqvB7LVu2zPZ48uTJuvrqq/Xggw86sSI0RWRkpNauXWubrq2t1ddf\nf63u3bsrLy9Pw4cPlyTl5+erX79+8vDw+E3bP/3uymheGvs/W1BQoIceeshZpcGBFi5cqKioqPO2\nO/31AudraGjQfffdp9GjR2vBggXy9PTUli1b9OCDD8rPz09Dhw5tdP1ffvnFQZXiQvhP+xuN44hh\nMxAXF6fs7Gy7eVlZWRo2bJjdvOzsbCUmJqpv37667bbbtGPHDknSvn371LdvXy1btkwDBgxQZGSk\nnn76adt6+/bt01133aXevXvr9ttvt/tm7NixY0pLS9OwYcMUGhqquLg45ebmSpLuvPNOvfzyy7a2\nFRUV6tWrl8rKyi7438AsCgoKzjj6cPqRnZ9++kmTJk1SRESE4uLitGbNGlu7L774QiNGjFB4eLhG\njBihDz74wLYsKytLsbGxCgsLO+Oo186dO3X33XdrwIABCg0N1b333iuLxaL9+/fr2muv1cGDB21t\nV6xYoYkTJxrx1Ju9yMhIFRUV6dixY5KkzZs3q0ePHoqPj9eGDRts7fLz8zVo0CBVVFToscceU//+\n/TVkyBC98sortjZ1dXV68sknFRYWptjYWOXn59uWFRQUaMSIEZo3b54iIiIUHR2tV1991bac10Dz\nU19fb/t93f79++v111+3LQsJCdG//vUv2/Tp3zCPHTtWqampGjBggFJSUlRVVaUHHnhAERERiomJ\n0ZNPPqm6ujpJjNPN1b59+xQWFqbU1FSFh4crKytLY8eO1cqVK23L6TfnKi8v1759+5SUlCRPT09J\nUt++ffXYY4/p+PHjjfbDTTfdJEkaM2aMcnNztXjxYk2ePNm27e+//14hISGSTo7dCQkJmjBhgiIi\nIrRlyxb63wnO19/n68PG3n937typMWPGKDQ0VCkpKaqsrLQt++WXXzR16lTFxMTohhtu0MiRI/X1\n119LkoYOHWr3Of7bb79VeHj4bzqzqLkgGDqZi4uLEhISVFBQoIqKCklSdXW1CgsL7U49y8vL06xZ\ns5Senq78/HyNHj1a9957rw4fPixJqqqq0r59+/TZZ5/pxRdf1Jtvvqnt27dLOvlB5aqrrlJBQYEe\ne+wxbdy40bbd1157TSUlJXrvvfe0detW3XTTTZozZ46kk78Z+dFHH9nafvzxxwoLC1P79u0N/7u0\nZOc6clRfX6+UlBRdffXV+uKLL7Rw4UItWLBABQUFkqTHH39cf/zjH1VQUKDHH39cs2fPVnV1tXbv\n3q0ZM2Zo3rx5+vLLL+Xi4mJ7LUnSww8/rNjYWP3jH//Qhg0bVFVVpTfeeEOBgYHq3bu3Pv74Y1vb\nDz/80LS/ERocHKwOHTrYBvoNGzZo0KBBGjhwoDZt2iTp5Bv99u3bNXDgQD322GNyd3fXZ599phUr\nVigrK0vvvfeeJGn+/Pnas2ePcnNztXLlSn3++ed2+/ruu+/Url07bd68WU8++aQyMzN18OBBXgPN\nVEVFhVq3bq2NGzdq3rx5evbZZ21h+nxHgnfu3KlPPvlEzz33nF5//XW5ubnpiy++0N///nft3LlT\nWVlZkhinm7MjR47o8ssv1xdffHHG0Qj6zfkCAgIUHh6uu+++W4sWLVJ+fr5qa2s1evRoJSQknLUf\nMjIyJMn2xdvq1attl+78+v/06dPFxcVKSEjQpk2b1Lt3bz300EP0v4Odr7+lxvvwXO+/dXV1uv/+\n+zV8+HAVFhZqzJgxtvdeSfrTn/4kV1dXffzxxyosLFTv3r31/PPPS5KSkpLs+vPDDz9UfHz8bz6z\nqDkgGDYDAQEB6tu3r3JyciRJ69atU3R0tN0LKisrSzfeeKP69OkjV1dXjRo1SsHBwVq/fr2tzYQJ\nE+Th4aFevXqpW7du2rt3r0pLS7Vz505NmTJF7u7uCg0NVWJiom2dO+64QwsXLpS3t7f2798vX19f\n2wee+Ph4lZSUqKSkRNLJI5ann+6KC+uf//ynDhw4oClTpsjNzU3du3fXzTffrFWrVkmSWrVqpays\nLH355Zfq06ePvvrqK/n6+ionJ0eDBg1SWFiY3N3dNXnyZHl5edm2+9prr+n2229XbW2tfvrpJ7Vr\n187Wx0lJSbbTJ0tLS/Xdd98Zcl3rxaJ///4qLCyUdPLLmKioKF1zzTXy8PDQjh07tG3bNgUGBsrL\ny0t5eXmaPn26WrVqpcDAQN177722vvr44481ceJE+fn5qVOnTho/frzdftzd3XXffffJ1dVVsbGx\n8vHxUWlpKa+BZsrT01P33XefXFxcNGjQIPn6+mrfvn2STp7W1JjBgwfL19dXrVu3VqtWrVRUVKSs\nrCzV1dVpzZo1GjVqFON0MzFlyhSFh4erb9++Cg8Pt7tWLDk5We7u7nb/r+i35mPZsmUaO3asCgoK\nNH78eIWHh+vRRx/VL7/8ctZ++Pnnn+3Wb+pPeru5uSkxMVGtWrXS/v37VVRURP87QWP9fT7nev/d\nunWr6urqdM8998jNzU1DhgyxO8PrkUce0cyZM+Xq6qp9+/apbdu2tv5MTk7WP/7xD9v9CD788MOL\ntj+5xtDJTg1GiYmJWrNmjcaMGaOsrCxNmjTJ7oYXhw8f1jXXXGO3bmBgoO20BRcXF7Vr1862zN3d\nXQ0NDSorK5OPj498fHxsyzp37qwffvhBklRZWanZs2drx44duuKKK3T55ZfbamrdurWioqK0du1a\njR49Wt98842WLl1qzB8C2r9/v6qqqhQeHi7p5Gujvr5ePXr0kHTyw/3ChQv16KOPqra2Vrfccose\nffRRlZWVqWPHjrbteHh42E1v375d48ePV01Nja6++mpVVlYqICBAkjR8+HDNnTtX+/fv19q1azVk\nyBC7Dz5mExkZqbffflvfffedGhoadPXVV0uSBg4cqC+++EJ1dXUaOHCg9u/fr4aGBg0dOlQNDQ1y\ncXFRfX29/P39JUmHDh2y64POnTvb7adNmzZyc3OzTZ/6/8proHny9fWVq+v/fY/q4eGhEydONGnd\nDh062B5PnDhRLi4uev311/X444+rT58+mjNnjiwWC+N0MzB//vwzrjHct2+fXFxcznokh/fX5sPT\n01Pjxo3TuHHjVFdXp6+++krPPfecnnjiCaWmpp6zH36rNm3a2L60p/+d51z9/fjjj+vaa69tdN1z\nvf+WlZWd8f/89PfuAwcOaO7cudqzZ4+6deumtm3bqr6+XpLUrVs3XXXVVVq/fr26dOmi+vp69e3b\n9wI+Y8chGDYTQ4cOVXp6uoqKilRaWqqwsDC765oCAwNt31Cf8uOPP6pPnz6Nbrdjx46qqalRZWWl\n2rZtK0l21xPNmjVLV155pV555RW5uLiosLDQ7rSyESNGaOHChfLz81N0dLR8fX0vwLM1L1dXV7tz\nzo8fP277AqBDhw669NJL9emnn9qWWywWNTQ0qK6uTv/+97/17LPPSpK2bdumBx54QD179lTHjh1V\nVFRkW8dqtdpOMT548KCmT5+uv/3tb+rZs6ekk6cjnnpz8vPz08CBA5WTk6NPPvlEDz/8sLF/gGau\nf//+evLJJ7Vx40YNGjTINn/QoEFatWqVjh8/rgkTJqhjx45yd3fXF198IXf3k8NoVVWVqqurJZ38\nf3fq+j1JTb7jHa+Bi8+v/0839o31t99+qxEjRmjixIk6dOiQnnrqKc2ZM0dpaWmqrq5mnG7GznbK\nMO+vzcPatWuVmZlpO4PK09NTf/jDH/THP/5R6enp5+2H0/36/3N5ebnd8tNfB/S/c5yvv3v27Nlo\nH55Lx44ddfDgQduXvdLJ/uzUqZOkk0cMb7vtNtv1xe+//76+//572/pJSUn65JNPFBQUZHfk+GLD\nqaTNhI+Pj6KiojRt2jTbOdKnGzlypP7+979r69atOnHihFavXq1//etfio2NlXTu0yA6d+6sPn36\n6JlnnlFdXZ127Nhhu6ZFOnnthJeXl1xcXPTTTz/phRdekCTbt+FRUVE6ePCgVq9efdEeFm9Orrji\nCtXW1io/P1/19fVatmyZ7RunG264QV5eXnrttddktVp14MAB3XnnnVq5cqVcXFw0ZcoU2y3SO3To\nYDtKnJCQoC+//FKbNm2S1WrVkiVLbAHl1L+njgBt3LhRH3/8sd0db5OTk7VmzRr9/PPPGjBggCP/\nHM2On5+funXrprfeessuGEZGRurbb7/Vd999p/DwcF166aUKCwvTs88+q2PHjumXX37Rgw8+qPnz\n50s6+f/1pZde0qFDh3To0CG7i9sbw2vg4hMUFGS7ocTnn3+ubdu2nbPtO++8o1mzZunIkSPy8/OT\nl5eX2rVrxzjdzPH+2rz1799fNTU1mjt3riwWiyTphx9+0IoVKxQTE6Pq6upG+8HDw8P2BW1QUJB2\n7Nihn3/+WUeOHNFf/vKXc+6X/neO8/V3ly5dmtyHp+vdu7f8/Py0ePFiWa1Wbdy40e7+ANXV1fL2\n9pYk7dmzx/Y+fUpSUpIKCgr06aefXtT9STB0stO/fUpOTtaePXvOeuOHsLAwpaWlacaMGerbt69W\nrVqlV1991fZNRmMX2s6fP1+HDh1Sv379NHPmTLuL51NTU/Xpp5+qd+/eGjdunKKjo+Xt7a09e/ZI\nOjlgxsXF6aeffrL7oIym+XW/dOzYUY899pimT59uG9xOnSLs7u6ul19+WQUFBYqMjNTo0aPVv39/\nPfDAA/Lw8NDixYv1t7/9Tb1799Ztt92mcePG6Q9/+IO6deum559/Xk899ZTCw8NVVlamK664QtLJ\n0xvuv/9+jRs3ThEREXr55Zd166232vpXOvn7Xfv371d8fLzd6XJmNWDAAP3888/q37+/bV7r1q3V\ntWtXXX/99ba7oGVmZurw4cOKiYlRfHy8LrvsMs2cOVOS9MADD6hPnz5KTEzUmDFjFBkZ2eg+T71O\neA04X1N+VuT0NjNmzNAnn3yisLAwvfnmm3YfCH69rSlTpqh169YaMmSI+vfvr8rKSk2fPl2S9MIL\nLzBOO1Fj/c77a/Pm7++vN998UwcPHlRSUpJCQ0N1zz33qFevXpo+fbqmT5/eaD/cdNNNuvvuu/X+\n++9r6NChGjhwoEaMGKGRI0cqOjq60X0vWLCA/new8/X3b+3DX7//bt68WX379tWrr76qwYMH29ql\np6fr1VdfVVhYmCZPnqwbb7xRFovFdqO39u3b64YbbpCnp6e6d+9u2PM3mkvD7z3RGqaxdOlSHThw\nwGE/zg7Hi4uL05/+9Cddf/31zi4FTsJr4OLGOH1xot/Mjf5vWWbMmKErrrjijBvOXUy4xhDnZLFY\nVFpaqrfffluLFi1ydjkwQGlpqTZu3ChPT08CgUnxGri4MU5fnOg3c6P/W5aff/5Ze/bs0fr16+1+\nX/hiZO5zhtCor776SnfddZeSkpJ03XXXObscGODZZ5/V0qVLlZ6e7uxS4CS8Bi5ujNMXJ/rN3Oj/\nluWjjz7SAw88oAcffNDuTtQXI04lBQAAAACT44ghAAAAAJgcwfD/t3f3YVHV+f/HXzhA3Jgi4U1u\nJUW1bObiIIjmHYKKDo7mqpVtdqsi1dbXbtbc/CVhWW2lu5bmZtvX7rRac90gN1G/q9BqsCyaV970\nNZCi2jVgEOVGcJDfH17Nt1lGQ+IwOOf5uC6va87nfM7wPu9rHHjNOXMOAAAAAJgcwRAAAAAATI5g\nCAAAAAAmRzAEAAAAAJMjGAIAAACAyREMAQAAAMDkCIYAAAAAYHIEQwAAAAAwOYIhAAAAAJgcwRAA\nAAAATI5gCAAAAAAmRzAEAAAAAJMjGAIAAACAyREMAQAAAMDkCIYAAAAAYHIEQwAAAAAwOYIhAAAA\nAJgcwRAAAAAATI5gCAAAAAAmRzAEAAAAAJMjGAIAAACAyREMAQAAAMDkCIYAAAAAYHIEQwAAAAAw\nOYIhAAAAAJic4cFw06ZNstlsslqtstvt2rp1q8d5aWlpiomJUWxsrKxWq2JjY13roqOjZbVaVVdX\n57aN0+lUQkKCkpOTDd0HAAAAAPBl/kY+eWlpqR599FGtWbNGMTEx2rVrl+bMmaO8vDyFhYW5zT1w\n4IDWrVuna665xuNzBQUFadu2bbLb7a6xvLw8OZ1OI3cBAAAAAHyeoUcMIyMjtXPnTsXExMjpdKq8\nvFxdu3ZVQECA2zyHwyGHw6Err7zyjM+VkpKi7Oxst7GsrCyNGzfOkNoBAAAAwCwMP5X2s2vCAAAg\nAElEQVQ0ODhYX331lWJiYvTII49o3rx5Cg0NdZuzf/9+hYaGKi0tTUOHDtXNN9+sPXv2uNb7+fnJ\nZrOpoKBA1dXVkqTa2loVFhYqKSnJ6F0AAAAAAJ/WIRef6du3r/bu3atXX31VTz31lPLz893WNzQ0\nyGq1auHChcrNzZXdbtfs2bNVWVnpmhMeHq74+Hjl5ORIkrZs2aLExMQWRx8BAAAAAOemQ4Jhly5d\nZLFYNGTIEKWkpLS4AE1ycrJWrVqlqKgoBQQEaMaMGerTp48rQDY3N0uSUlNTXaeTZmVladKkSR1R\nPgAAAAD4NEOD4Y4dO3THHXe4jZ08eVLdunVzG9u8ebP++te/uo01NjbqggsucBsbO3asPv30U+3b\nt09lZWWKi4trdS1OZ9M5Vg8AAAAA5mDoVUn79++vffv26f3335fdbldubq5yc3N17733us2rq6vT\n888/r6uvvlr9+vXTmjVr1NDQoGHDhrnNCwkJ0ahRozR//nzZbLZzqqWqqu6HJwEAAACAj+rZ88Iz\nrjM0GEZEROill17SkiVLlJmZqcjISK1cuVKXX365Fi1aJD8/P2VkZGjKlCkqLy/XrFmzdPToUfXv\n31+rV69WUFCQpNMXn/mO3W7X3XffreXLlxtZOgAAAACYhl/zd1/g83Hl5ce9XQIAAAAAeM3Zjhh2\nyMVnAAAAAACdF8EQAAAAAEyOYAgAAAAAJkcwBAAAAACTIxgCAAAAgMkZersKAAAAAOgsmpqaVFpa\n4u0yDBUZeYUsFss5b0cwBAAAAGAKpaUlKl2zQ5dd1NfbpRjiy8pvpNulqKirznlbgiEAAAAA07js\nor6K6nWZt8vodPiOIQAAAACYHMEQAAAAAEyOYAgAAAAAJkcwBAAAAACT4+IzAAAAgA/y9VsztPW2\nDPCMYAgAAIDzmq8HIKltIai0tESfrflMl4b73hU4yxxftvm2DPCMYAgAAIDzWmlpiba+/7l69+zn\n7VIMcaT8C42Z1LYQdGn4ZbqiV5QBVcHXEAwBAABw3uvds58u6UsAAtqKi88AAAAAgMkRDAEAAADA\n5AiGAAAAAGByBEMAAAAAMDmCIQAAAACYHMEQAAAAAEyOYAgAAAAAJkcwBAAAAACTIxgCAAAAgMn5\ne7sAAAAAtE5TU5NKS0u8XYahIiOvkMVi8XYZgOkQDAEAAM4TpaUlemrTfnXrfam3SzHEsSNlWmCT\noqKu8nYpgOkQDAEAAM4j3Xpfqh59r/B2GQB8jOHfMdy0aZNsNpusVqvsdru2bt3qcV52drbGjBkj\nq9WquXPnqrKy0rUuOjpaVqtVdXV1bts4nU4lJCQoOTnZ0H0AAAAAAF9maDAsLS3Vo48+qqeeekq7\nd+/Wb37zG82bN09Hjx51m3fw4EFlZGRo2bJlys/PV0REhBYsWOA2JygoSNu2bXMby8vLk9PpNHIX\nAAAAAMDnGRoMIyMjtXPnTsXExMjpdKq8vFxdu3ZVQECA27zvjhYOGDBAgYGBeuihh5SXlyeHw+Ga\nk5KSouzsbLftsrKyNG7cOCN3AQAAAAB8nuGnkgYHB+urr75STEyMHnnkEc2bN0+hoaFuc0pKShQV\nFeVaDgsLU/fu3VVS8n9X3bLZbCooKFB1dbUkqba2VoWFhUpKSjJ6FwAAAADAp3XIfQz79u2rvXv3\n6tVXX9VTTz2l/Px8t/X19fUKDg52GwsODtaJEydcy+Hh4YqPj1dOTo4kacuWLUpMTGxx9BEAAAAA\ncG46JBh26dJFFotFQ4YMUUpKSosL0AQFBbmFQOl0WPzPI4upqamu00mzsrI0adIkYwsHAAAAABMw\n9HYVO3bs0Jo1a/Tf//3frrGTJ0+qW7dubvOioqJ0+PBh17LD4dCxY8fcTi+VpLFjxyozM1P79u1T\nWVmZ4uLitH379lbV0qNHiPz9uVkqAAA4f1VVdZV03NtlGCo8vKt69rzwnLY53ZcqYwrqJNralzrV\nG1SR97W1JxUG1dNZtKUvksHBsH///tq3b5/ef/992e125ebmKjc3V/fee6/bvIkTJ2rmzJmaOnWq\n+vfvr6VLl2rkyJEtAmRISIhGjRql+fPny2aznVMtVVV1PzwJAACgE3M4arxdguEcjhqVl59b+KUv\nZ97Gl9ETz87Wl7MFRkNPJY2IiNBLL72k1157TfHx8XrhhRe0cuVKXX755Vq0aJEyMjIknb5P4eLF\ni7VgwQINGzZMFRUVWrJkiet5/Pz8XI/tdruKi4s5jRQAAAAA2omhRwwladCgQXrvvfdajD/++ONu\ny+PHj9f48eM9PseBAwdcj0ePHu22nJiYqMTExPYpFgAAAABMqEMuPgMAAAAA6LwIhgAAAABgcgRD\nAAAAADA5giEAAAAAmBzBEAAAAABMjmAIAAAAACZHMAQAAAAAkyMYAgAAAIDJEQwBAAAAwOQIhgAA\nAABgcv7eLgAAAOA/NTU1qbS0xNtlGCoy8gpZLBZvlwEAkgiGAACgEyotLdED2TkK6XWxt0sxRN23\n/9LSieMUFXWVt0sBAEkEQwAA0EmF9LpYXfte4u0yAMAU+I4hAAAAAJgcRwwBAPAyX/8+Hd+lA4DO\nj2AIAICXlZaW6P7sdxTcq6e3S2l39d+W6/cTb+S7dADQyREMAQDoBIJ79VTXvr55oRUAQOfHdwwB\nAAAAwOQIhgAAAABgcgRDAAAAADA5giEAAAAAmBzBEAAAAABMjmAIAAAAACZHMAQAAAAAk+M+hgCA\nDtPU1KTS0hJvl2GoyMgrZLFYvF0GAADnhGAIAOgwpaUlum/TSgX3Dvd2KYaoP+LQctvdioq6ytul\nAABwTgiGAIAOFdw7XF379vR2GQAA4Hv4jiEAAAAAmJzhwbCwsFA33HCD4uLiNG7cOL3zzjse56Wl\npSkmJkaxsbGyWq2KjY11rYuOjpbValVdXZ3bNk6nUwkJCUpOTjZ0HwAAAADAlxl6KumxY8d0zz33\naNGiRbLZbNq/f7/uuOMOXXbZZRo6dKjb3AMHDmjdunW65pprPD5XUFCQtm3bJrvd7hrLy8uT0+k0\nchcAAAAAwOcZesTwm2++UWJiomw2myTpmmuuUUJCgnbv3u02z+FwyOFw6Morrzzjc6WkpCg7O9tt\nLCsrS+PGjWv/wgEAAADARAwNhtHR0XrmmWdcy9XV1SosLNTPfvYzt3n79+9XaGio0tLSNHToUN18\n883as2ePa72fn59sNpsKCgpUXV0tSaqtrVVhYaGSkpKM3AUAAAAA8HkddvGZ48ePa+7cuRowYIBG\njx7ttq6hoUFWq1ULFy5Ubm6u7Ha7Zs+ercrKStec8PBwxcfHKycnR5K0ZcsWJSYmKiAgoKN2AQAA\nAAB8UofcrqKsrEzp6enq16+fli1b1mJ9cnKy2wVkZsyYobVr1yo/P182m03Nzc2SpNTUVG3YsEHT\np09XVlaW0tPTVVNT06oaevQIkb8/NxwGAG+qqurq7RIMFx7eVT17XnhO2/h6X+iJZ23vy3FjCuok\n2t6XKmMK6iTa2pc61RtUkfe1tScVBtXTWbSlL1IHBMN9+/Zp9uzZmjx5subPn+9xzubNm3Xq1ClN\nmDDBNdbY2KgLLrjAbd7YsWOVmZmpffv2qaysTHFxcdq+fXur6qiqqvvhSQAAQzkcrfsw73zmcNSo\nvPzc/nD39b7QE8/oi2f0xTP60hI98exsfTlbYDQ0GFZUVGj27Nm68847NWvWrDPOq6ur0/PPP6+r\nr75a/fr105o1a9TQ0KBhw4a5zQsJCdGoUaM0f/581wVtAAAAAAA/jqHB8L333lNVVZVWrlypFStW\nSDp9IZlbb71VVVVV8vPzU0ZGhqZMmaLy8nLNmjVLR48eVf/+/bV69WoFBQW5tvmO3W7X3XffreXL\nlxtZOgAAAACYhqHBMC0tTWlpaa2aO2fOHM2ZM8fjugMHDrgejx492m05MTFRiYmJP6pOAAAAADCz\nDrsqKQAAAACgcyIYAgAAAIDJEQwBAAAAwOQIhgAAAABgcgRDAAAAADA5giEAAAAAmBzBEAAAAABM\nztD7GAKAWTU1Nam0tMTbZRgqMvIKWSwWb5cBAADaAcEQAAxQWlqixzbNUdfewd4uxRA1R+qVaXtZ\nUVFXebsUAADQDgiGAGCQrr2D1b1vqLfLAAAA+EF8xxAAAAAATI5gCAAAAAAmRzAEAAAAAJMjGAIA\nAACAyREMAQAAAMDkCIYAAAAAYHIEQwAAAAAwOYIhAAAAAJgcwRAAAAAATI5gCAAAAAAmRzAEAAAA\nAJPz93YBAM5vTU1NKi0t8XYZhoqMvEIWi8XbZQAAABiGYAjgRyktLdHa92Yromewt0sxREV5vW6e\nulpRUVd5uxQAAADDEAwB/GgRPYPV++JQb5cBAACANuI7hgAAAABgcgRDAAAAADA5giEAAAAAmJzh\nwbCwsFA33HCD4uLiNG7cOL3zzjse52VnZ2vMmDGyWq2aO3euKisrXeuio6NltVpVV1fnto3T6VRC\nQoKSk5MN3QcAAAAA8GWGBsNjx47pnnvu0e23367CwkL97ne/09KlS7Vr1y63eQcPHlRGRoaWLVum\n/Px8RUREaMGCBW5zgoKCtG3bNrexvLw8OZ1OI3cBAAAAAHyeocHwm2++UWJiomw2myTpmmuuUUJC\ngnbv3u0277ujhQMGDFBgYKAeeugh5eXlyeFwuOakpKQoOzvbbbusrCyNGzfOyF0AAAAAAJ9naDCM\njo7WM88841qurq5WYWGhfvazn7nNKykpUVRUlGs5LCxM3bt3V0nJ/90022azqaCgQNXV1ZKk2tpa\nFRYWKikpychdAAAAAACf12EXnzl+/Ljmzp2rAQMGaPTo0W7r6uvrFRzsfnPs4OBgnThxwrUcHh6u\n+Ph45eTkSJK2bNmixMREBQQEGF88AAAAAPiwDgmGZWVlmjFjhsLDw/XCCy+0WB8UFOQWAqXTYTE0\n1P2G2ampqa7TSbOysjRp0iTjigYAAAAAk/A3+gfs27dPs2fP1uTJkzV//nyPc6KionT48GHXssPh\n0LFjx9xOL5WksWPHKjMzU/v27VNZWZni4uK0ffv2VtXRo0eI/P0tbd4PQJKamppUXFzs7TIMExUV\nJYvl3P6fVFV1NaiaziM8vKt69rzwnLahL57RF898vS/0xLO29+W4MQV1Em3vS5UxBXUSbe1LneoN\nqsj72tqTCoPq6Sza0hfJ4GBYUVGh2bNn684779SsWbPOOG/ixImaOXOmpk6dqv79+2vp0qUaOXKk\nunXr5jYvJCREo0aN0vz5810XtGmtqqq6H54E/IDi4kPa9k6a+kSEeLuUdvfvijol3/gHRUVddU7b\nORw1BlXUeTgcNSovP7c/xOjLmbfxdfSlJXriGX3xjL54Rl9aoieena0vZwuMhgbD9957T1VVVVq5\ncqVWrFghSfLz89Ott96qqqoq+fn5KSMjQ9HR0Vq8eLEWLFigyspKxcXFacmSJa7n8fPzcz222+26\n++67tXz5ciNLB86oT0SILukT+sMTAQAAgPOEocEwLS1NaWlprZo7fvx4jR8/3uO6AwcOuB6PHj3a\nbTkxMVGJiYk/qk4AAAAAMLMOuyopAAAAAKBzIhgCAAAAgMkRDAEAAADA5FoVDBcvXtxi7Ey3ngAA\nAAAAnF/OevGZRx99VGVlZfr000916NAh17jT6dTx4759Dx0AAAAAMIuzBsP09HR9/fXXevLJJ3Xv\nvfe6xi0WS4ubzwMAAAAAzk9nDYaXXHKJLrnkEr3//vuqqanR8ePH1dzcLEmqq6tTWFhYhxQJAAAA\nADBOq+5j+Ic//EF/+MMf3IKgn5+ftm3bZlhhAAAAAICO0apg+Kc//Ulbt25VeHi40fUAAAAAADpY\nq4LhxRdfrO7duxtdCzqRpqYmlZaWeLsMQ0VGXiGLxeLtMgAAAACva1UwjIyM1M0336yEhAQFBga6\nxr9/QRr4ltLSEh167T5delGot0sxRFllrXTbckVFXeXtUgAAAACva1Uw7N27t3r37m10LehkLr0o\nVFf0utDbZQAAAAAwWKuCIUcGAQAAAMB3tSoYRkdHy8/Pz22sV69e2rFjhyFFAQAAAAA6TquC4cGD\nB12PT548qa1bt2rPnj2GFQUAAAAA6DhdznWDgIAATZgwQR9//LER9QAAAAAAOlirjhhu3LjR9bi5\nuVmHDh1SQECAYUUBAAAAADpOq4Jhfn6+23KPHj20bNkyQwoCAAAAAHSsVgXDp556SidPntThw4fV\n1NSkq666Sv7+rdoUAAAAANDJtSrdffrpp7rvvvsUFhamU6dOqaKiQitWrFBMTIzR9QEAAAAADNaq\nYPjEE09o2bJlriC4Z88eLV68WOvXrze0OAAAAACA8Vp1VdK6ujq3o4MDBw5UQ0ODYUUBAAAAADpO\nq4Jh9+7dtXXrVtfy1q1bFRYWZlhRAAAAAICO06pTSRcvXqy0tDQ9+uijrrG3337bsKIAAAAAAB2n\nVUcMc3NzFRwcrL/97W967bXXFB4eroKCAqNrAwAAAAB0gFYFw3fffVfr1q1TSEiIoqOjtWHDBr35\n5ptG1wYAAAAA6ACtCoYnT55UQECAa/n7jwEAAAAA57dWfcdwzJgxuu222zRhwgRJUk5OjpKTkw0t\nDAAAAADQMVoVDB9++GF9+OGH+sc//iF/f3/deuutGjNmzDn9oL179+qee+5RXl6ex/VpaWn6+OOP\nZbFY1NzcLD8/PxUVFUmSoqOjFRwcrL///e8KCQlxbeN0OjVs2DB17dpV27ZtO6d6AAAAAACntSoY\nStL48eM1fvz4Nv2Q9evX65lnnpG//5l/3IEDB7Ru3Tpdc801HtcHBQVp27ZtstvtrrG8vDw5nc42\n1QQAAAAAOK1V3zH8MVatWqU333xT6enpZ5zjcDjkcDh05ZVXnnFOSkqKsrOz3caysrI0bty4dqsV\nAAAAAMzI8GA4bdo0bdy4Uddee+0Z5+zfv1+hoaFKS0vT0KFDdfPNN2vPnj2u9X5+frLZbCooKFB1\ndbUkqba2VoWFhUpKSjJ6FwAAAADApxkeDCMiIn5wTkNDg6xWqxYuXKjc3FzZ7XbNnj1blZWVrjnh\n4eGKj49XTk6OJGnLli1KTEzkCqkAAAAA8CO1+juGRkpOTna7yumMGTO0du1a5efny2azqbm5WZKU\nmpqqDRs2aPr06crKylJ6erpqampa9TN69AiRv7+lxXhTU5OKi4vbZ0c6qaioKFksLff9bKqquuob\ng+rpLMLDu6pnzwvPaZuqqq4GVdM50BPP6Itn9MUz+tISPfGs7X05bkxBnUTb+1JlTEGdRFv7Uqd6\ngyryvrb2pMKgejqLtvRF6iTBcPPmzTp16pTrdhiS1NjYqAsuuMBt3tixY5WZmal9+/aprKxMcXFx\n2r59e6t+RlVVncfx4uJD+uKtt9Xvop5trr8z+6KyXI5f3qSoqKvOaTuHo3WB+3zmcNSovPzcfrn6\nel/oiWf0xTP64hl9aYmeeEZfPKMvntGXluiJZ2fry9kCY6cIhnV1dXr++ed19dVXq1+/flqzZo0a\nGho0bNgwt3khISEaNWqU5s+fL5vN1m4/v99FPRXV++J2ez4AAAAAOJ94LRguWrRIfn5+ysjI0JQp\nU1ReXq5Zs2bp6NGj6t+/v1avXq2goCBJpy8+8x273a67775by5cv91bpAAAAAOBTOiwYDh48WLt2\n7XItP/74427r58yZozlz5njc9sCBA67Ho0ePdltOTExUYmJi+xYLAAAAACZi+FVJAQAAAACdG8EQ\nAAAAAEyOYAgAAAAAJkcwBAAAAACTIxgCAAAAgMkRDAEAAADA5AiGAAAAAGByBEMAAAAAMDmCIQAA\nAACYHMEQAAAAAEyOYAgAAAAAJkcwBAAAAACTIxgCAAAAgMkRDAEAAADA5AiGAAAAAGByBEMAAAAA\nMDmCIQAAAACYHMEQAAAAAEyOYAgAAAAAJkcwBAAAAACTIxgCAAAAgMkRDAEAAADA5AiGAAAAAGBy\nBEMAAAAAMDmCIQAAAACYHMEQAAAAAEyOYAgAAAAAJtdhwXDv3r0aMWLEGddnZ2drzJgxslqtmjt3\nriorK13roqOjZbVaVVdX57aN0+lUQkKCkpOTDasbAAAAAHxdhwTD9evX66677pLT6fS4/uDBg8rI\nyNCyZcuUn5+viIgILViwwG1OUFCQtm3b5jaWl5d3xucEAAAAALSO4cFw1apVevPNN5Wenn7GOd8d\nLRwwYIACAwP10EMPKS8vTw6HwzUnJSVF2dnZbttlZWVp3LhxhtUOAAAAAGZgeDCcNm2aNm7cqGuv\nvfaMc0pKShQVFeVaDgsLU/fu3VVSUuIas9lsKigoUHV1tSSptrZWhYWFSkpKMq54AAAAADABw4Nh\nRETED86pr69XcHCw21hwcLBOnDjhWg4PD1d8fLxycnIkSVu2bFFiYqICAgLat2AAAAAAMJlOcVXS\noKAgtxAonQ6LoaGhbmOpqamu00mzsrI0adKkDqsRAAAAAHyVv7cLkKSoqCgdPnzYtexwOHTs2DG3\n00slaezYscrMzNS+fftUVlamuLg4bd++vVU/o0ePEPn7W1qMV1V1lcPDfF8SHt5VPXteeE7bVFV1\n1TcG1dNZtLUvvoyeeEZfPKMvntGXluiJZ23vy3FjCuok2t6XKmMK6iTa2pc61RtUkfe1tScVBtXT\nWbSlL1InCYYTJ07UzJkzNXXqVPXv319Lly7VyJEj1a1bN7d5ISEhGjVqlObPny+bzXZOP6Oqqs7j\nuMNR0+a6zxcOR43Ky8/tlwh9OfM2voyeeEZfPKMvntGXluiJZ/TFM/riGX1piZ54dra+nC0weu1U\n0kWLFikjI0PS6fsULl68WAsWLNCwYcNUUVGhJUuWuOb6+fm5HtvtdhUXF3MaKQAAAAC0kw47Yjh4\n8GDt2rXLtfz444+7rR8/frzGjx/vcdsDBw64Ho8ePdptOTExUYmJie1bLAAAAACYSKe4+AwAAAAA\nwHsIhgAAAABgcgRDAAAAADA5giEAAAAAmBzBEAAAAABMjmAIAAAAACZHMAQAAAAAkyMYAgAAAIDJ\nEQwBAAAAwOQIhgAAAABgcgRDAAAAADA5giEAAAAAmBzBEAAAAABMjmAIAAAAACZHMAQAAAAAkyMY\nAgAAAIDJEQwBAAAAwOQIhgAAAABgcgRDAAAAADA5giEAAAAAmBzBEAAAAABMjmAIAAAAACZHMAQA\nAAAAkyMYAgAAAIDJEQwBAAAAwOQIhgAAAABgcgRDAAAAADA5giEAAAAAmJzhwXD//v2aPn26rFar\npkyZok8++cTjvLS0NMXExCg2NlZWq1WxsbGuddHR0bJaraqrq3Pbxul0KiEhQcnJyYbuAwAAAAD4\nMkODYWNjo9LT0zVt2jQVFhbqlltuUXp6uurr61vMPXDggNatW6eioiLt3r1bRUVFbuuDgoK0bds2\nt7G8vDw5nU4jdwEAAAAAfJ6hwfDjjz+WxWLRjTfeKIvFoqlTp+qiiy7Sjh073OY5HA45HA5deeWV\nZ3yulJQUZWdnu41lZWVp3LhxhtQOAAAAAGZhaDAsKSlRVFSU29jll1+ukpISt7H9+/crNDRUaWlp\nGjp0qG6++Wbt2bPHtd7Pz082m00FBQWqrq6WJNXW1qqwsFBJSUlG7gIAAAAA+DxDg2F9fb2Cg4Pd\nxoKDg3XixAm3sYaGBlmtVi1cuFC5ubmy2+2aPXu2KisrXXPCw8MVHx+vnJwcSdKWLVuUmJiogIAA\nI3cBAAAAAHyev5FP7ikE1tfXKyQkxG0sOTnZ7QIyM2bM0Nq1a5Wfny+bzabm5mZJUmpqqjZs2KDp\n06crKytL6enpqqmpaVUtPXqEyN/f0mK8qqqrHOe6Y+eZ8PCu6tnzwnPapqqqq74xqJ7Ooq198WX0\nxDP64hl98Yy+tERPPGt7X44bU1An0fa+VBlTUCfR1r7UqeW1PXxFW3tSYVA9nUVb+iIZHAyvuOIK\nvfXWW25jhw8f1qRJk9zGNm/erFOnTmnChAmuscbGRl1wwQVu88aOHavMzEzt27dPZWVliouL0/bt\n21tVS1VVncdxh6N1wfJ85nDUqLz83H6J0Jczb+PL6Iln9MUz+uIZfWmJnnhGXzyjL57Rl5boiWdn\n68vZAqOhp5IOGTJEjY2Neuutt+R0OrV+/Xo5HA4NHz7cbV5dXZ2efPJJFRcXy+l06pVXXlFDQ4OG\nDRvmNi8kJESjRo3S/PnzZbPZjCwdAAAAAEzD0GAYGBio1atXKysrSwkJCVq7dq1eeuklBQUFadGi\nRcrIyJAkTZkyRbfeeqtmzZql+Ph4bd++XatXr1ZQUJCk0xef+Y7dbldxcXGLo44AAAAAgLYx9FRS\nSbr66qv19ttvtxh//PHH3ZbnzJmjOXPmeHyOAwcOuB6PHj3abTkxMVGJiYntUywAAAAAmJChRwwB\nAAAAAJ0fwRAAAAAATI5gCAAAAAAmRzAEAAAAAJMjGAIAAACAyREMAQAAAMDkCIYAAAAAYHIEQwAA\nAAAwOYIhAAAAAJgcwRAAAAAATI5gCAAAAAAmRzAEAAAAAJMjGAIAAACAyREMAQAAAMDkCIYAAAAA\nYHIEQwAAAAAwOYIhAAAAAJgcwRAAAAAATI5gCAAAAAAmRzAEAAAAAJMjGAIAAACAyREMAQAAAMDk\nCIYAAAAAYHIEQwAAAAAwOYIhAAAAAJgcwRAAAAAATI5gCAAAAAAmZ3gw3L9/v6ZPny6r1aopU6bo\nk08+8TgvOztbY8aMkdVq1dy5c1VZWelaFx0dLavVqrq6OrdtnE6nEhISlJycbOg+AAAAAIAvMzQY\nNjY2Kj09XdOmTVNhYaFuueUWpaenq76+3m3ewYMHlZGRoWXLlik/P18RERFasGCB25ygoCBt27bN\nbSwvL09Op9PIXQAAAAAAn2doMPz4449lsVh04403ymKxaOrUqbrooou0Y8cOt7ARMOwAABPBSURB\nVHnfHS0cMGCAAgMD9dBDDykvL08Oh8M1JyUlRdnZ2W7bZWVlady4cUbuAgAAAAD4PEODYUlJiaKi\notzGLr/8cpWUlJx1XlhYmLp37+42z2azqaCgQNXV1ZKk2tpaFRYWKikpycA9AAAAAADfZ2gwrK+v\nV3BwsNtYcHCwTpw4cc7zwsPDFR8fr5ycHEnSli1blJiYqICAAIOqBwAAAABz8Dfyyc8UAkNCQtzG\ngoKCPM4LDQ11G0tNTdWGDRs0ffp0ZWVlKT09XTU1NT+6zi8qy3/0c3RWX1SWq18bty2rrG3XWjqT\nsspaXdXGbf9dUffDk85D/66oU/82bltRXv/Dk85TP2bfao74bl9+zL7VH3H88KTz1I/Zt/pvffN3\n0Y/Zr7pv/9WOlXQup/dtQJu2PXakrH2L6URO79s1bdr2SPkX7VtMJ3Kk/AsN0JVt2rbM8WU7V9M5\nlDm+1E/10zZt+2XlN+1cTefxZeU3imzjX7l+zc3Nze1cj0tubq4WL16sLVu2uMbsdrvuv/9+jRkz\nxjX23HPPqaqqSk8++aQkyeFwaPjw4fr444/VrVs3RUdHKzs7W3379tWIESP0+uuva968ecrJydH2\n7du1ePHiFhemAQAAAAC0jqGnkg4ZMkSNjY1666235HQ6tX79elfo+76JEycqJydHRUVFamho0NKl\nSzVy5Eh169bNbV5ISIhGjRql+fPny2azGVk6AAAAAJiGocEwMDBQq1evVlZWlhISErR27Vq99NJL\nCgoK0qJFi5SRkSHp9H0KFy9erAULFmjYsGGqqKjQkiVLXM/j5+fnemy321VcXKxJkyYZWToAAAAA\nmIahp5ICAAAAADo/Q48YAgAAAAA6P4IhAAAAAJgcwRDnha+++srbJQAAAAA+i2DYjkpLS3X33Xdr\n8ODBGjRokK6//nqtX7/e22V1Grt27dLtt9+uQYMGKSEhQTNnzmzVbUYOHDigm266qQMq7DjR0dGu\niy99X1JSknbs2NHxBXUS0dHRslqtio2NldVq1YgRI/TYY4/p2LFj3i6tU7nvvvv04osversMr0tK\nSlJMTIxiY2Ndr5nY2Fi3WySdaTtf/X/23f+hujr3+606nU4lJCQoOTnZS5V1Lg8//LCuvfZalZf7\n5r0jW4PXSuvxnntmrenNn//8Z02dOrWDKvIeX3hfIRi2k+bmZs2aNUs///nP9dFHH+mf//ynHn30\nUT377LM/+EeKGWRlZem//uu/ZLfblZubq507d+q2227TY489ptdff/2s2x47dkxNTU0dVGnHeffd\nd/XRRx95u4xOxc/PT+vXr1dRUZF2796t9evX69tvv9WcOXO8XRo6qeXLl6uoqMj1mikqKtLYsWO9\nXZZXBQUFtfjQLS8vT06n00sVdS7Hjh1Tbm6uJkyYoHXr1nm7HK/itYKO8v07DPgiX3lfIRi2k6qq\nKn399deaOHGiAgMDJUnx8fF6+OGHdfLkSUnS2rVrlZKSoiFDhuhXv/qVKioqJEkFBQWaOHGiHn/8\nccXGxmrs2LHatGmT1/alvTU0NOiJJ57QE088oalTpyo0NFQWi0VjxozR0qVL9eyzz8rhcOh///d/\nNXPmTFcPsrKy5HA4NGfOHFVVVSk2NlbV1dXe3p12M23aNC1YsOCMR8MqKyv14IMPasiQIRo9erSe\nffZZnTx5UqWlpRowYICOHz/umrt8+XI98MADHVW6YZqbm/X9CyX37t1bS5cu1aFDh7R9+3ZJ//d6\nGjlypEaOHKlnnnnG9UdMc3OzXnzxRY0cOVKDBw/Wvffeq6NHj3pjVzrE0aNH9eCDDyopKUkDBw7U\n5MmTtXv3bkmnP6GdNWuWfv3rX2vQoEEaN26c/vKXv3i54o7z2WefaebMmYqPj9ekSZNaHCHcuXOn\nJkyYoPj4eGVmZqqxsdFLlba/lJQUZWdnu41lZWVp3LhxbmOvv/667Ha74uLiNHz4cLdP/aOjo5WZ\nmanBgwfr5Zdf7pC6O8rGjRsVHx+vX/7yl3r33Xdd7x8vvviiHnjgAd12222yWq268cYbdfDgQUmn\nf0/bbDbNmTNHCQkJ+sc//uHNXWg3P/a1smLFCt15551uc3/xi1/oww8/NLZwL1mwYIF++9vfupa3\nb9+upKQkSbznnq03ZnC295X77rvPNe/QoUOKjo52La9bt06JiYkaPny4nnvuOSUnJ3v1/YVg2E7C\nw8M1ePBg3XHHHXrhhReUn5+v+vp6TZs2TTabTX/961/1yiuvaOXKlcrNzdUll1yiefPmubb//PPP\ndcEFFyg/P18ZGRl65JFHVFxc7MU9aj9FRUU6ceKExzeIhIQE9erVS1u3btXcuXN13XXXqaCgQEuX\nLtWiRYtUXV2t1atXq0ePHioqKlL37t29sAftz8/PTzNnztRVV12lRYsWeZxzzz33qEuXLvrb3/6m\nd955RwUFBXrhhRcUGRmp/v37KycnxzX3gw8+0PXXX99R5XeokJAQxcbG6p///Kck6emnn9bhw4eV\nnZ2tv/zlL9q3b59WrVolSXr77bf1/vvv64033tDf//53BQUF6YknnvBm+YZ69tln1aVLF3344Ycq\nLCxUbGysnn/+edf6jz76SCNGjNA//vEP3XLLLVq8eLFPBaAzqa2t1V133aXU1FQVFBTo//2//6df\n//rX+uKLL1xzdu7cqTVr1mjz5s3au3evVq5c6cWK24+fn59sNpsKCgpcH6TV1taqsLDQ7T24sLBQ\nL7/8slauXKnCwkL97ne/04oVK1RWVuaa09jYqJ07d+qWW27p8P0w0p/+9CdNmzZNAwcOVHh4uFuI\n+fDDDzVjxgwVFhZq5MiRuvvuu11/4JWUlMhmsyk3N1eDBg3yVvntpj1eK3a7XQUFBaqqqpJ0ukdf\nfvmlqQLB94+EmfU990x8/Sjh953tfeU/+/Dd8q5du7Rs2TK9+OKL+p//+R/V1NTom2++6dC6/xPB\nsB2tXr1aM2fOVEFBgWbPnq3BgwfrwQcf1NGjR/Xee+/ptttuU1RUlAIDAzVv3jx98sknrj9UQkND\n9cADDyggIEDDhg3TiBEj9Ne//tXLe9Q+KisrFRYWJovF4nF9RESEjh49qvr6eqWnp8vf318DBgzQ\n2rVr1atXrw6utmM0NzfLz89PS5Ys0UcffaQPPvjAbX1ZWZk++eQTLVy4UMHBwerVq5fuv/9+bdiw\nQZI0adIk1zZ79+5VTU2Nhg8f3uH70VG6d+/u+sPlz3/+sx5++GF169ZNPXr00L333qt33nlHkrRp\n0ybNnDlT/fr1U0BAgBYuXKj09HRvlm6oBx54QIsWLVKXLl309ddfq1u3bjpy5Ihrfd++fWW329Wl\nSxddf/31qq2tlcPh8GLF7W/evHkaPHiw4uPjNXjwYC1YsEA7duxQRESEbrrpJvn5+Sk+Pl5JSUmu\n/z+SlJ6ert69eys8PFxz5871qbM0wsPDFR8f7/rwaMuWLUpMTFRAQIBrzoABA/Tee+/p0ksvVWVl\npU6ePKmgoCC3109qaqr8/f0VEhLS4ftglKKiIh0/flyjRo2SJN1000168803XeuHDh2q8ePHy2Kx\nKD09XbW1tSoqKpIkWSwWpaam6oILLlCXLr7x59OPfa1cdtllbh9Ubtq0SWPHjnWdOWU2ZnjPRUs/\n9L5yJllZWZoyZYquvfZaBQYGav78+Wf8W7mj+Hv1p/uYwMBA3Xrrrbr11lvV2Niof/7zn3ruuef0\nm9/8Rv/6179cn7JJp4OBxWLRN998I4vFoj59+ri9kfbp0+e8/vLq91100UWqrKyU0+mUv3/Ll9zX\nX38tPz8/9ezZ0238+4fafVWfPn20cOFCZWZmKi4uzjVeWVmp4OBgtyOkffv2VWVlpZqampSamuo6\nBTc7O1upqak+84eKJ1VVVfrJT34ih8OhEydOaObMma5P3E6dOqWmpiY1NjaqoqJCffr0cW0XFham\nsLAwb5VtuCNHjujJJ59UcXGxrrjiCnXr1k2nTp1yrQ8PD3c99vf3V3Nzs9t6X7Bs2TLXL+PvvPLK\nK/r88881ePBgSaffb5uampSSkuKa07dvX9djX3q//e5U7NTUVG3YsEHTp09XVlaW0tPTVVNT45rn\n5+enFStWKCcnRxEREbr22mvdtpdOf2jna959911VVVVpxIgRkk5faKW6ulr79++XJF122WWuuV26\ndFGvXr1UUVGhiIgIXXjhhW6B6XzXXq+VSZMmadOmTbrxxhuVnZ19xrNgzMAM77lo6YfeV87k22+/\n1dVXX+1aDg4O9vrfLATDdrJp0yYtXbpUW7dulXQ6JA4dOlS/+tWvlJmZqX79+umuu+7SL37xC9c2\nxcXF6tevn4qKilRRUeE6iiSdDksDBw70yr60t0GDBqlbt26uT0a+Ly8vT9XV1bJarfrjH//otm7t\n2rWuX0C+bPLkydq2bZt+85vfuMYuvvhi1dfXq7q62hUOy8rK1L17d1ksFnXv3l3Dhw/X5s2b9eGH\nH+qll17yVvmGq6mp0e7du3XXXXcpLCxMgYGB+vOf/6xLLrlEknTixAmVl5crMDBQvXv3djvi8dVX\nX2njxo269957vVV+u9i4caPq6+s1Y8YMSad/6QQFBWnevHmaMWOG3nrrLde8Q4cOebPUTqFnz56y\nWq164403XGNHjhxRUFCQa/m773hLp99vvx8UfcHYsWOVmZmpffv2qaysTHFxca7v6UrSq6++qs8/\n/1zbtm1TaGionE5nizMXfO00sJqaGn344Yd67bXXdOmll7rGn3zySb3xxhv6yU9+om+//dY13tTU\npG+//VZ9+vSR0+n0uX5858e+ViZMmKDf/va3+vjjj1VbW6shQ4Z4YS/a15nec7t06eK6boQk1ym0\nZkJv3P3Q+8qll156xr5cfPHFbqeOnjhxwuvXRfDdQwwd7LrrrlNdXZ2WLFniOm3giy++0BtvvKGk\npCRdf/31evXVV/Xll1/q1KlTeuONN3TTTTepvr5eklRdXa2XX35ZTqdTO3bsUH5+vlJTU725S+0m\nMDBQjz32mJ5++mm99957qqmp0YkTJ7R582Y98sgjevDBBxUTE6Pu3bvr5ZdfVlNTk/bu3avf//73\n6tq1qwIDA9XQ0OD2H8vXPP744/rss8/0r3/9S9Lpi64MHTpUS5YsUV1dnY4cOaIXXnhBkyZNcm0z\nefJkvfzyy7rwwgvVv39/b5VuqLKyMj300EP6+c9/ruuuu05dunSR3W7Xc889p+PHj6uurk4LFy7U\nggULJEl2u11vvPGGysrK1NDQoOXLl+vLL7/08l78eLW1tVq3bp1qamr05Zdf6pNPPlF0dLRqa2sV\nHBws6fQHTX/84x+5mqCkxMRElZSU6IMPPtCpU6dUXFys6dOnuz64k6RVq1apvLxcR44c0apVqzRt\n2jQvVtz+QkJCNGrUKM2fP182m63F+traWgUEBMjf31+1tbV6+umn5XQ6ffr1s3HjRkVGRmrgwIG6\n6KKLXP+mTp2qDz74QFVVVcrNzdWuXbvkdDr14osvqkePHrJard4u3VA/9rUSHh6uIUOG6Omnn1Zq\naqpPBOj/fM/ds2ePoqOjFRkZqfz8fNXU1KiystL1NQYzoTfufuh9JTIyUnv37tW3336rmpoavfba\na65tr7/+ev3lL3/Rp59+qsbGRi1btszrV+EnGLaTsLAwrV27VkeOHNHEiRNltVp15513KiYmRo88\n8ogmT56sG264wfXdw6ysLNcf9ZLUrVs3/fvf/9awYcP07LPPavny5W6fPJzvxo8frxUrVmjTpk1K\nSkrSiBEj9OabbyozM1O33367AgICtGrVKu3cuVNDhgzRr3/9az355JO64oor9NOf/lRXXnmlhgwZ\n4nZhhPPZf/7i7NGjhxYvXuw29txzz+nkyZNKTk7WlClTFB8fr4ceesi1ftSoUTpx4oQmT57cITV3\nBD8/P02fPl2xsbGKi4vTHXfcocsvv9x1cRlJevTRR9WjRw+lpqZq9OjRqqur07JlyyRJU6dO1dSp\nU3Xbbbdp5MiRampq0mOPPeat3Wk306dPV3R0tJKTk3XjjTdq2rRpGj58uDIzM/XKK68oLi5O9913\nn6ZMmSKHw3HGq/f6wh9s33em/enevbteeeUVrVu3TgkJCbrrrrv0y1/+0nUfLT8/Pw0dOlTTpk3T\n1KlTNXLkSN12220dWbphvt8Tu92u4uJitw+UvnPHHXfIYrG4vlN38uRJxcbGui565muvFen0xSEm\nTpzYYvy6665TeHi4/vSnPykmJkavvPKKEhISVFRUpJdfftkneyG132vlu+0/++wz2e32DqndaP/5\nnjt9+nQNHz5cN910ky677DIlJSXplltu8Rikv88XXzvt1Rtf8UPvK4cPH9aIESM0adIkTZ48WYmJ\nia45gwYN0q9+9SvNnTtXSUlJCg4OlsVi8eop637N3/9CAbyioKBA999/v3bt2uXtUgAAMKUXX3xR\nhw4d0u9//3tvl3LeKSoq0sKFC33qIk6A0Q4fPqyAgAC3r8YMHDhQmzdvVr9+/bxSE0cMAQAAcM4a\nGhr02WefaeXKlbrhhhu8XQ5wXjlw4IDS09NVVVWlkydP6qWXXtJll13mtVAocfEZAAAAtMGxY8c0\nY8YMDRw4UDfffLO3ywHOKzabTQcPHpTdbteJEyfUv39/r19MkFNJAQAAAMDkOJUUAAAAAEyOYAgA\nAAAAJkcwBAAAAACTIxgCAAAAgMkRDAEAAADA5AiGAAAAAGBy/x8ohfF9FdVf1QAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c965710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create plots comparing demand per different time categories:\n",
    "fig = plt.figure(figsize = (15,16))\n",
    "formatter = FuncFormatter(millions_format)\n",
    "\n",
    "plt.subplot(3,1,1) #this plot could have a polar format, although I'm not sure it'd be a better viz.\n",
    "ax1 = sns.countplot(df_viz.hour, order = range(24))\n",
    "ax1.yaxis.set_major_formatter(formatter)\n",
    "plt.tick_params(labelsize=13)\n",
    "plt.title('Total Number of Trips Between 9/1/2014 and 8/31/2015', fontsize=17, color='navy')\n",
    "plt.xlabel('Hour', fontsize=13)\n",
    "\n",
    "plt.subplot(3,1,2)\n",
    "ax2 = sns.countplot(df_viz.weekday, order = range(7))\n",
    "ax2.set_xticklabels(weekday_labels)\n",
    "ax2.yaxis.set_major_formatter(formatter)\n",
    "plt.tick_params(labelsize=13)\n",
    "plt.xlabel('')\n",
    "\n",
    "plt.subplot(3,1,3) #same as the line plot but with less granularity, perhaps easier to visualize the MOM growth\n",
    "ax3 = sns.countplot(df_viz.month)\n",
    "ax3.set_xticklabels(month_labels)\n",
    "ax3.yaxis.set_major_formatter(formatter)\n",
    "#plt.text(0, 3100000, 'Consistent growth in the number of trips from month to month!', color='navy', size=15)\n",
    "plt.tick_params(labelsize=13)\n",
    "plt.xlabel('')\n",
    "\n",
    "#fig.subplots_adjust(hspace=.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimated Monthly Base Revenue: how much was the NYC market worth in the period?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create dataframe with grouped revenue data:\n",
    "byDateRev = df_viz.groupby('month')['est_revenue'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$595M Base Revenue   Uber's Base Gross Margin: $148,757,755\n"
     ]
    }
   ],
   "source": [
    "#Estimate Uber's gross margin for the entire 1 year period based on 25% share of the base revenue.\n",
    "sumRev = byDateRev.sum()\n",
    "print '${:,.0f}M'.format(sumRev * 1e-6), \"Base Revenue   Uber's Base Gross Margin:\", '${:,.0f}'.format(sumRev * .25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "208735400.25"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Estimated base gross margin based on information that average UberX fare in Sep 2014 was $27 (but it has dropped):\n",
    "len(df_viz) * 27 * .25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "1    4.145715e+07\n",
       "2    4.725285e+07\n",
       "3    5.215439e+07\n",
       "4    5.409507e+07\n",
       "5    6.153991e+07\n",
       "Name: est_revenue, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byDateRev.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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x44MwNNSlffs3k1HktJwP8fJyY8qUo0yYcIS6dR04ciQ8w6yEP/5YnQ0brlC3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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ae92450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create chart showing total base revenue per month.\n",
    "months_seq = [9,10,11,12,1,2,3,4,5,6,7,8]\n",
    "formatter = FuncFormatter(millions_currency)\n",
    "\n",
    "plt.figure(figsize = (14, 7))\n",
    "ax = sns.barplot(byDateRev.index, byDateRev.values, order = months_seq)\n",
    "ax.set_xticklabels(month_labels)\n",
    "ax.yaxis.set_major_formatter(formatter)\n",
    "\n",
    "font = {'color': 'navy', 'size': 15} #text parameters\n",
    "plt.text(0, 55000000, 'Total Base Revenue for the Period: ${:,.0f} Million'.format(sumRev * 1e-6), \n",
    "         fontdict = font) #params: x, y, text, fontdict\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Revenue (Millions)', fontsize = 15)\n",
    "plt.tick_params(labelsize=13)\n",
    "plt.title('Estimated Monthly Base Revenue from Sep-2014 to Aug-2015', fontsize = 17, color='navy')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.241869694747738"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mean fare from revenue estimate for the period: $19.24 (assumes all trips are Uber X)\n",
    "df_viz.est_revenue.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21565512.055648196"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rough estimate of revenue share from Uber Pool from 12/1/2014 to 8/31/2015 (total of 25,634,526 trips):\n",
    "rev = df_viz[(df_viz.pu_date >= datetime.date(2014, 12, 1)) & (df_viz.pu_date <= datetime.date(2015, 8, 31))]\\\n",
    ".est_revenue.mean() #$19.17 mean revenue\n",
    "\n",
    "u_pool = rev * 0.75 * 1500000\n",
    "u_pool #$21,565,512 estimated revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04388612451815961"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u_pool/df_viz[(df_viz.pu_date >= datetime.date(2014, 12, 1)) & (df_viz.pu_date <= datetime.date(2015, 8, 31))]\\\n",
    ".est_revenue.sum() #Uber Pool represented only about 4% of the total revenue for the period between Dec/14 and Aug/15."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Month over Month Base Revenue Growth: how fast has Uber grown in the period?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sep Revenue = $29,932,282 Growth % =  0.0\n",
      "Oct Revenue = $35,531,001 Growth % =  18.7\n",
      "Nov Revenue = $38,170,687 Growth % =  7.4\n",
      "Dec Revenue = $41,661,569 Growth % =  9.1\n",
      "Jan Revenue = $41,457,151 Growth % =  -0.5\n",
      "Feb Revenue = $47,252,852 Growth % =  14.0\n",
      "Mar Revenue = $52,154,385 Growth % =  10.4\n",
      "Apr Revenue = $54,095,066 Growth % =  3.7\n",
      "May Revenue = $61,539,912 Growth % =  13.8\n",
      "Jun Revenue = $63,667,666 Growth % =  3.5\n",
      "Jul Revenue = $63,607,348 Growth % =  -0.1\n",
      "Aug Revenue = $65,961,099 Growth % =  3.7\n"
     ]
    }
   ],
   "source": [
    "#Create dictionary with calculated month over month revenue percentage growth:\n",
    "dict_growth = {}\n",
    "for i in range(len(months_seq)):\n",
    "    mo = months_seq[i]\n",
    "    rev = byDateRev[mo]\n",
    "    if mo == 9:\n",
    "        growth_pct = 0\n",
    "    elif mo == 1:\n",
    "        growth_pct = ((rev/byDateRev[12]) - 1) * 100\n",
    "    else:\n",
    "        growth_pct = ((rev/byDateRev[mo - 1]) - 1) * 100\n",
    "    print month_labels[i], 'Revenue =', '${:,.0f}'.format(rev), 'Growth % = ', '{:.1f}'.format(growth_pct)\n",
    "    dict_growth[month_labels[i]] = growth_pct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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++fpm099/X9TYsXVs5RI7n8SITVQe7ezh5s1wW7KZ3NttQmJ/m/iOZadP35Rh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\nLmQNtDnQd9MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dbbc450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Annotate Total Cummulative Growth for the Period\n",
    "\n",
    "df_plt1 = pd.DataFrame(pd.Series(dict_growth), index = month_labels, columns = ['growth_pct'])\n",
    "\n",
    "labels_list = df_plt1.growth_pct.values.round(1)\n",
    "kwargs = {'fontsize': 12, 'ha':'center', 'va': 'bottom', 'weight': 'bold', 'color': 'navy'}\n",
    "\n",
    "ax = df_plt1.plot(kind = 'bar', figsize = (15,7), fontsize = 14, rot = 0, legend=False)#df_plt2[1:] to exclude Sep\n",
    "ax.set_axis_bgcolor('#F9F9F9')\n",
    "ax.get_yaxis().set_ticks([])\n",
    "annotate_labels(ax, labels_list, **kwargs)\n",
    "\n",
    "plt.axhline(0, color='yellow')\n",
    "plt.title('Month Over Month Percentage Growth of Base Revenue From September 2014', \n",
    "          fontsize = 17, color='navy')\n",
    "plt.ylabel('Growth %', fontsize = 15, weight='bold', color='navy')\n",
    "plt.ylim(-2, 22)\n",
    "plt.tick_params(labelsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cummulative % Growth Over Period: 83.6883959769\n"
     ]
    },
    {
     "data": {
      "image/png": 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RHKmp3yA5OQATJ7ZGdnZeqcve6tb9Qbrybmk5F1988T8IgoDXX/fCoEHaq+YbN17H+fPR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11addee90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot Cummulative Base Revenue Growth Over the Period\n",
    "print 'Cummulative % Growth Over Period:', df_plt1.growth_pct.sum()\n",
    "\n",
    "df_plt1.cumsum().plot(color = 'navy', marker = 'D', legend = False, figsize=(16, 6))\n",
    "plt.title('Cummulative Revenue Growth Percentage from September 2014', fontsize=17, weight='bold', color='navy')\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Which day of the week has the worst (slowest) traffic overall?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Create dataframe with grouped data.\n",
    "df_plt2 = df_viz.groupby('weekday')['trip_mph_avg'].median() #used median since distribution is skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAWEBxcNN3p/frV09ixzfT997v10Ufr053XNN1ZWcYh+AIAAFjYunWHtXLl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\n5a0kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a9de710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create plot. Does Saturday have better traffic flow than Monday?\n",
    "\n",
    "fig = plt.figure()\n",
    "labels_list = df_plt2.values.round(1)\n",
    "kwargs = {'fontsize': 12, 'ha':'center', 'va': 'bottom', 'weight': 'bold', 'color': 'navy'}\n",
    "\n",
    "ax = df_plt2.plot(kind='bar', rot = 0, figsize = (12, 6), fontsize = 12, edgecolor='yellow', linewidth=1)\n",
    "ax.set_xticklabels(weekday_labels, weight='bold')\n",
    "ax.set_axis_bgcolor('#F9F9F9')\n",
    "ax.get_yaxis().set_ticks([])\n",
    "annotate_labels(ax, labels_list, **kwargs)\n",
    "\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Median Trip Speed (mph)', fontsize = 14)\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.title('Which day of the week has the worst traffic?', fontsize = 18, color='navy')\n",
    "plt.show()\n",
    "#plot_url = py.plot_mpl(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is the median distance traveled per trip? Are there any insights we can learn from it?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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M/fv1SkyM4uLifNwZAPiWTw8Dbt68uebNm6cTJ04oPDxcy5cvV3R0tNthKEnp6WeLrBkR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e38f310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Why is the median distance traveled around 5 AM much longer than at the other times?\n",
    "#Could it be picking up riders that would otherwise use public transportation to the outer boroughs? \n",
    "#While the subway runs 24/7 in NYC, bus services don't. It's clear though that most customer are within Manhattan.\n",
    "plt.figure(figsize = (16,6))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "df_viz[(df_viz.weekday >= 5)].groupby('hour')['trip_distance'].median().plot(kind='bar', rot = 0, fontsize=14)\n",
    "plt.ylim(0, 9)\n",
    "plt.tick_params(labelsize=12)\n",
    "plt.ylabel('Median Trip Distance (miles)', fontsize=14, weight='bold', color='navy')\n",
    "plt.xlabel('Hour', fontsize=14, weight='bold', color='navy')\n",
    "plt.title('Weekend', fontsize=14, weight='bold', color='navy')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "df_viz[df_viz.weekday < 5].groupby('hour')['trip_distance'].median().plot(kind='bar', rot = 0)\n",
    "plt.tick_params(labelsize=12)\n",
    "plt.xlabel('Hour', fontsize=14, weight='bold', color='navy')\n",
    "plt.title('Weekdays', fontsize=14, weight='bold', color='navy')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AALiVG3YfYQAAAACA6+Tm5iolJdluWVqaWampFtvzkJCa8vDwKO5oNxyNMAAA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qOjc03r3r17ruc1a9bUjz/+aCknAABKM2ozAKCks9QIJyU95648AAAAAAAoEpbvZXDmTIbmzv1F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\nGZLCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b35dc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create plot similar to the above but with the count of trips per hour, comparing weekday vs weekend and \n",
    "#distances >=5 versus < 5 miles. Indeed, the plots show that there is a higher demand for trips that go beyond \n",
    "#miles (outside Manhattan) from 5 to 7 AM on weekdays, and from 6 to 8 AM on weekends (Saturday and Sunday).\n",
    "\n",
    "fig = plt.figure(figsize = (16,12))\n",
    "formatter = FuncFormatter(thousands_format)\n",
    "\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "ax1 = df_viz[(df_viz.weekday < 5) & (df_viz.trip_distance >= 5)].\\\n",
    "groupby('hour')['trip_distance'].count().plot(kind='bar', rot = 0)\n",
    "ax1.yaxis.set_major_formatter(formatter)\n",
    "ellipse = Ellipse(xy=(5, 150000), width=4, height=200000, edgecolor='red', fc='None', lw=1.5)\n",
    "ax1.add_patch(ellipse)\n",
    "plt.xlabel('Hour', fontsize=14, weight='bold', color='navy')\n",
    "plt.ylabel('Demand (number of trips)', fontsize=14, weight='bold', color='navy')\n",
    "plt.ylim(0, 1400000)\n",
    "plt.title('Weekday, 5 miles or more', fontsize=14, weight='bold', color='navy')\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "ax2 = df_viz[(df_viz.weekday < 5) & (df_viz.trip_distance < 5)].\\\n",
    "groupby('hour')['trip_distance'].count().plot(kind='bar', rot = 0)\n",
    "ax2.yaxis.set_major_formatter(formatter)\n",
    "ellipse = Ellipse(xy=(5, 150000), width=4, height=200000, edgecolor='red', fc='None', lw=1.5)\n",
    "ax2.add_patch(ellipse)\n",
    "plt.xlabel('Hour', fontsize=14, weight='bold', color='navy')\n",
    "plt.title('Weekday, less than 5 miles', fontsize=14, weight='bold', color='navy')\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "ax3 = df_viz[(df_viz.weekday >= 5) & (df_viz.trip_distance >= 5)].\\\n",
    "groupby('hour')['trip_distance'].count().plot(kind='bar', rot = 0)\n",
    "ax3.yaxis.set_major_formatter(formatter)\n",
    "ellipse = Ellipse(xy=(6.5, 50000), width=4, height=80000, edgecolor='red', fc='None', lw=1.5)\n",
    "ax3.add_patch(ellipse)\n",
    "plt.xlabel('Hour', fontsize=14, weight='bold', color='navy')\n",
    "plt.ylabel('Demand (number of trips)', fontsize=14, weight='bold', color='navy')\n",
    "plt.ylim(0, 500000)\n",
    "plt.title('Weekend, 5 miles or more', fontsize=14, weight='bold', color='navy')\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "ax4 = df_viz[(df_viz.weekday >= 5) & (df_viz.trip_distance < 5)].\\\n",
    "groupby('hour')['trip_distance'].count().plot(kind='bar', rot = 0)\n",
    "ax4.yaxis.set_major_formatter(formatter)\n",
    "ellipse = Ellipse(xy=(6.5, 50000), width=4, height=80000, edgecolor='red', fc='None', lw=1.5)\n",
    "ax4.add_patch(ellipse)\n",
    "plt.xlabel('Hour', fontsize=14, weight='bold', color='navy')\n",
    "plt.title('Weekend, less than 5 miles', fontsize=14, weight='bold', color='navy')\n",
    "\n",
    "\n",
    "fig.subplots_adjust(hspace=0.4)\n",
    "plt.show()\n",
    "#plot_url = py.plot_mpl(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What are the most popular pickup and drop-off taxi zones?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#CREATE INSTEAD SIDE-STACKED PLOT WITH TOP 10 PICKUP AND DROP-OFF LOCATIONS? OR TRY TOYPLOT WITH VALUE FOR EACH BAR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SUuL2mI12u6/DRgBrtNulIfFmhwEgRLgtUllShK4wcreVO1ftyIUr8uGKfLQLxWJ9+PDh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\nIiIiItIY/wenmyS+7Y1R+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f558510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The top 5 locations for origin and destination are the same: 2A, 15, 4C, 1, and 6B (likely all in Manhattan).\n",
    "#The top location for pick up and drop off, 2A, accounts for more than the total trips for the next two popular \n",
    "#locations.\n",
    "fig = plt.figure(figsize = (16,8))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "ax1 = df_viz.origin_taz.value_counts(ascending = True).plot(kind = 'barh', color = 'navy')\n",
    "ax1.set_xticklabels(['0', '1M', '2M', '3M', '4M', '5M', '6M'])\n",
    "plt.tick_params(labelsize=12)\n",
    "plt.xlabel('Number of Trips per Origin Locations', fontsize = 16, color='navy')\n",
    "\n",
    "plt.subplot(1,2,2) #note: it won't account missing values (4% of data)\n",
    "ax2 = df_viz.destination_taz.value_counts(ascending = True).plot(kind = 'barh')\n",
    "ax2.set_xticklabels(['0', '1M', '2M', '3M', '4M', '5M', '6M'])\n",
    "plt.tick_params(labelsize=12)\n",
    "plt.xlabel('Number of Trips per Destination Locations', fontsize = 16, color='navy')\n",
    "\n",
    "plt.show()\n",
    "#plot_url = py.plot_mpl(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.439222645705\n",
      "0.415849856325\n"
     ]
    }
   ],
   "source": [
    "#Percentage of trips represented by the top five origins/destinations:\n",
    "print df_viz.origin_taz.value_counts().head(5).sum()/float(len(df_viz)) #13,583,249\n",
    "print df_viz.destination_taz.value_counts().head(5).sum()/float(len(df_viz[df_viz.destination_taz.isnull() == False]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6231353"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total pickups or drop-offs within any combination of the top 5 locations: 6231353 (20% of the total trips)\n",
    "len(df_viz[(df_viz.origin_taz.isin(['2A', '15', '4C', '1', '6B']) == True) & \\\n",
    "           (df_viz.destination_taz.isin(['2A', '15', '4C', '1', '6B']) == True)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2903860697677705"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#29% of all trips start or finish at 2A:\n",
    "len(df_viz[(df_viz.origin_taz == '2A') | (df_viz.destination_taz == '2A')])/(1.0*len(df_viz)) #8979830"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the most popular pick up and drop off location pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Define data to plot:\n",
    "df_plt3 = df_viz.pivot_table('id', aggfunc='count', index='origin_taz', columns='destination_taz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 29)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_plt3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Cy6YFDl49KikGALzW+GXE3jwjKUYbOy9Z2iP1mAVKjls5jlk5zktSj1mg5LiV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\nsUkhIiIiRfp/9OT4YImO338AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c4ec150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the origin versus destination matrix via a heatmap:\n",
    "\n",
    "fig = plt.figure(figsize=(9,9))\n",
    "\n",
    "cmap = sns.cubehelix_palette(8, start=2, rot=0, dark=0, light=.9, as_cmap=True) #'YlOrRd' #ggplot:'viridis'\n",
    "\n",
    "sns.heatmap(df_plt3, cmap=cmap, linewidths=0.1, cbar=False)\n",
    "\n",
    "plt.title('Total Trips by Pickup and Drof-Off Taxi Zones', fontsize=18, color='navy')\n",
    "plt.xlabel('Destination', fontsize=15, fontname='Tahoma', color='navy')\n",
    "plt.ylabel('Origin', fontsize=15, fontname='Tahoma', color='navy')\n",
    "plt.tick_params(labelsize=11)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "len(df_viz[(df_viz.origin_taz.isin(['2A', '15', '4C', '1', '6B']) == True) & \\\n",
    "           (df_viz.destination_taz.isin(['2A', '15', '4C', '1', '6B']) == True)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is the mean time and distance to destination from the most popular pick up location?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Plot the mean travel time from the most popular origin, to all possible destinations.\n",
    "df_plt4 = df_viz[df_viz.origin_taz == '2A'].groupby('destination_taz')['trip_distance'].mean().sort_values()\n",
    "df_plt5 = df_viz[df_viz.origin_taz == '2A'].groupby('destination_taz')['duration_min'].mean().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JWcODB48oWjTuGMa3HlpznBNTvLgXDRoUZsyYHaxceYL9+9vy6adFcHFJS//+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\niIiIiIiIpOLEk4iIiIiIiKT6P4wz97xslIcuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ac6b3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Assuming 18 is EWR, and given the average driving distance from 2A, this pick up location is probably located in \n",
    "#Midtown, not in Lower Manhattan (Penn Station lat/long: 40.750568, -73.993519)\n",
    "#Straight line distance: 11 miles, Driving distance: 15 miles Difference: 36%\n",
    "#The mean time and distance to zone 18 adds further evidence that it's the EWR airport (from Midtown).\n",
    "#Based on the distance from 2A, if the origin is Midtown, then 15 is in Upper Manhattan.\n",
    "\n",
    "fig = plt.figure(figsize = (16,12))\n",
    "kwargs = {'fontsize': 12, 'ha':'center', 'va': 'bottom', 'color': 'navy'}\n",
    "\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "ax1 = sns.barplot(x=df_plt4.index, y=df_plt4.values)\n",
    "ax1.set_axis_bgcolor('#F9F9F9')\n",
    "#ax1.set_yticks(range(0, 21, 2))\n",
    "ax1.get_yaxis().set_ticks([])\n",
    "labels_list = df_plt4.values.round(1)\n",
    "annotate_labels(ax1, labels_list, **kwargs)\n",
    "plt.title('Mean Distance to Destination from the Most Popular Pickup Zone: 2A', fontsize=17, color='navy')\n",
    "plt.ylabel('Mean Distance (Miles)', fontsize = 14)\n",
    "plt.xlabel('Drop-off Taxi Zone', fontsize=14)\n",
    "plt.tick_params(labelsize=12)\n",
    "\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "ax2 = sns.barplot(x=df_plt5.index, y=df_plt5.values)\n",
    "ax2.set_axis_bgcolor('#F9F9F9')\n",
    "#ax2.set_yticks(range(0, 51, 5))\n",
    "ax2.get_yaxis().set_ticks([])\n",
    "labels_list = df_plt5.apply(lambda x: '{:.0f}'.format(round(x))).values\n",
    "annotate_labels(ax2, labels_list, **kwargs)\n",
    "plt.title('Mean Time to Destination from the Most Popular Pickup Zone: 2A', fontsize=17, color='navy')\n",
    "plt.ylabel('Mean Time (Minutes)', fontsize = 14)\n",
    "plt.ylim(0,55)\n",
    "plt.xlabel('Drop-off Taxi Zone', fontsize=14)\n",
    "plt.tick_params(labelsize=12)\n",
    "\n",
    "\n",
    "fig.subplots_adjust(hspace=.5)\n",
    "plt.show()\n",
    "#plot_url = py.plot_mpl(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization of Variable Distributions and Bivariate Relationships."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of trip distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(568.57000000000005, 2593.0799999999999)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_viz.trip_distance.max(), df_uber.trip_distance.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([12709212,  8364039,  3566741,  2035261,  1296836,   672674,\n",
       "          705998,   747728,   299927,   132566]),\n",
       " array([  0. ,   2.5,   5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ,\n",
       "         22.5,  25. ]))"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq, bins_dist = np.histogram(df_viz.trip_distance, bins=10, range=(0, 25))\n",
    "freq, bins_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.416272624313\n",
      "0.273952505032\n",
      "0.116823658014\n",
      "0.0666621532187\n",
      "0.0424760657879\n",
      "0.0220325045555\n",
      "0.0231239859891\n",
      "0.024490794302\n",
      "0.00982369319139\n",
      "0.00434201559583\n"
     ]
    }
   ],
   "source": [
    "#69% of all trips had distance < 5 miles\n",
    "for val in freq:\n",
    "    print float(val)/sum(freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.09, 5.199632456547355)"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_viz.trip_distance.median(), df_viz.trip_distance.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.682138457729\n",
      "0.319235889888\n"
     ]
    }
   ],
   "source": [
    "print len(df_viz[df_viz.trip_distance <= 5])/(1.0 *len(df_viz)) #68% of trips are 5 miles or less\n",
    "print len(df_viz[df_viz.trip_distance <= 2])/(1.0 *len(df_viz)) #32% of trips are 2 miles or less"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>origin_taz</th>\n",
       "      <th>destination_taz</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>pu_date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>trip_mph_avg</th>\n",
       "      <th>est_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>943214</th>\n",
       "      <td>19594602</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>371.55</td>\n",
       "      <td>2014-09-16</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>474.283333</td>\n",
       "      <td>47.003549</td>\n",
       "      <td>818.761667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3620586</th>\n",
       "      <td>5138875</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>357.27</td>\n",
       "      <td>2014-11-04</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>309.516667</td>\n",
       "      <td>69.257014</td>\n",
       "      <td>736.103333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10581718</th>\n",
       "      <td>10072444</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330.90</td>\n",
       "      <td>2015-02-04</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>648.500000</td>\n",
       "      <td>30.615266</td>\n",
       "      <td>808.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11224977</th>\n",
       "      <td>17353914</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>378.01</td>\n",
       "      <td>2015-02-25</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>600.766667</td>\n",
       "      <td>37.752760</td>\n",
       "      <td>874.335833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11684172</th>\n",
       "      <td>23119455</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>350.15</td>\n",
       "      <td>2015-02-03</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>559.350000</td>\n",
       "      <td>37.559667</td>\n",
       "      <td>811.085000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13829799</th>\n",
       "      <td>16426984</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>568.57</td>\n",
       "      <td>2015-03-27</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>652.316667</td>\n",
       "      <td>52.296993</td>\n",
       "      <td>1225.858333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16530542</th>\n",
       "      <td>15478567</td>\n",
       "      <td>7A</td>\n",
       "      <td>17</td>\n",
       "      <td>315.57</td>\n",
       "      <td>2015-04-09</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>248.066667</td>\n",
       "      <td>76.327063</td>\n",
       "      <td>641.620833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18643154</th>\n",
       "      <td>6860856</td>\n",
       "      <td>5C</td>\n",
       "      <td>16</td>\n",
       "      <td>308.42</td>\n",
       "      <td>2015-05-31</td>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>304.183333</td>\n",
       "      <td>60.835680</td>\n",
       "      <td>648.749167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18854980</th>\n",
       "      <td>9245564</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>302.79</td>\n",
       "      <td>2015-05-10</td>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>441.666667</td>\n",
       "      <td>41.133736</td>\n",
       "      <td>687.015833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18949729</th>\n",
       "      <td>10208984</td>\n",
       "      <td>4A</td>\n",
       "      <td>7C</td>\n",
       "      <td>335.30</td>\n",
       "      <td>2015-05-09</td>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>813.666667</td>\n",
       "      <td>24.725113</td>\n",
       "      <td>874.108333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                id origin_taz destination_taz  trip_distance    pu_date  year  \\\n",
       "943214    19594602         10              15         371.55 2014-09-16  2014   \n",
       "3620586    5138875         14             NaN         357.27 2014-11-04  2014   \n",
       "10581718  10072444         16             NaN         330.90 2015-02-04  2015   \n",
       "11224977  17353914         15              15         378.01 2015-02-25  2015   \n",
       "11684172  23119455         14              14         350.15 2015-02-03  2015   \n",
       "13829799  16426984         15             NaN         568.57 2015-03-27  2015   \n",
       "16530542  15478567         7A              17         315.57 2015-04-09  2015   \n",
       "18643154   6860856         5C              16         308.42 2015-05-31  2015   \n",
       "18854980   9245564         15              15         302.79 2015-05-10  2015   \n",
       "18949729  10208984         4A              7C         335.30 2015-05-09  2015   \n",
       "\n",
       "          month  day  hour  weekday  duration_min  trip_mph_avg  est_revenue  \n",
       "943214        9   16    10        1    474.283333     47.003549   818.761667  \n",
       "3620586      11    4    23        1    309.516667     69.257014   736.103333  \n",
       "10581718      2    4    18        2    648.500000     30.615266   808.600000  \n",
       "11224977      2   25    15        2    600.766667     37.752760   874.335833  \n",
       "11684172      2    3    18        1    559.350000     37.559667   811.085000  \n",
       "13829799      3   27    19        4    652.316667     52.296993  1225.858333  \n",
       "16530542      4    9     1        3    248.066667     76.327063   641.620833  \n",
       "18643154      5   31     5        6    304.183333     60.835680   648.749167  \n",
       "18854980      5   10    22        6    441.666667     41.133736   687.015833  \n",
       "18949729      5    9    16        5    813.666667     24.725113   874.108333  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Vey long distance trips (>= 300 miles):\n",
    "df_viz[df_viz.trip_distance >= 300].head(10) #45 total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of trip duration (in minutes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.092376e+07\n",
       "mean     2.099231e+01\n",
       "std      1.608456e+01\n",
       "min      0.000000e+00\n",
       "25%      1.085000e+01\n",
       "50%      1.723333e+01\n",
       "75%      2.668333e+01\n",
       "max      9.593000e+02\n",
       "Name: duration_min, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Median trip duration is ~ 17 minutes\n",
    "df_viz.duration_min.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1235100, 5391133, 6311894, 5266617, 3929001, 2777037, 1878313,\n",
       "        1256118,  852522,  589907]),\n",
       " array([  0.,   5.,  10.,  15.,  20.,  25.,  30.,  35.,  40.,  45.,  50.]))"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq, bins_dur = np.histogram(df_viz.duration_min, bins=10, range=(0, 50))\n",
    "freq, bins_dur"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot of the histograms for trip distance and duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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q1UsDBw5UkyZN9PbbbysmJkbnzp0z9mndunWBk/a2bdvUsmXL0j0wAAAqmGvX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\n/h+UhgfB7ROGPwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bab76d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Here we plot the distribution of trip duration next to the histogram of trip distance for comparison.\n",
    "fig = plt.figure(figsize=(16, 7))\n",
    "\n",
    "formatter = FuncFormatter(millions_format)\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "ax1 = df_viz.trip_distance.dropna().hist(bins=bins_dist)\n",
    "ax1.yaxis.set_major_formatter(formatter)\n",
    "\n",
    "plt.xlabel('Distance (miles)', fontsize=14, weight='bold')\n",
    "plt.ylabel('Counts', fontsize=14, weight='bold')\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.title('Distribution of Trip Distance', color='navy', fontsize=16)\n",
    "\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "ax2 = df_viz.duration_min.hist(bins=bins_dur)\n",
    "ax2.yaxis.set_major_formatter(formatter)\n",
    "ax2.set_xticks(bins_dur) #bins are in 5 minutes intervals.\n",
    "\n",
    "plt.xlabel('Duration (minutes)', fontsize=14, weight='bold')\n",
    "plt.ylabel('')\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.title('Distribution of Trip Duration', color='navy', fontsize=16)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trip Distance versus Trip Duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.4180704 ,  10.56080168],\n",
       "       [  3.65081289,  17.97784922],\n",
       "       [  5.09364295,  22.49040273],\n",
       "       ..., \n",
       "       [ 19.84848465,  44.5050345 ],\n",
       "       [ 12.19842835,  29.74001046],\n",
       "       [  2.52056516,   9.87329898]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create data to plot with average distance and duration grouped by origin-destination location pairs:\n",
    "df_plt6 = df_viz.groupby(['origin_taz', 'destination_taz'])['trip_distance', 'duration_min'].mean()\n",
    "df_plt6.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(812, 2)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_plt6.shape #distance, duration (x, y): Fit function to replace 7 incorrect duration entries with estimate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZrTEYDEye/DuTJ6c97ydPHiN+fm40blyc/v3rWlyw31lnqVKerFnTkQEDttKu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VFdCNO0OEm2Tz6Gxfo1CAhoR49BaGpqbRprdHBEP9LcWJfE3eiHjaD+y2+6rEXqjNKKcp5esZTT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\nS4vZknwQX1dXRg8e2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8PfzZ0AhQVCWBiCnR0Avpu34Bbct9PjHh47EdvEXRw9mYW7SeTJ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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b2cfad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create plot.\n",
    "distance = df_plt6.values[:,:1]\n",
    "duration = df_plt6.values[:,1:]\n",
    "duration_hat = pd.Series(distance.reshape(812,)).apply(lambda x: 10*np.sqrt(x))\n",
    "\n",
    "plt.figure(figsize = (14,7))\n",
    "cmap = sns.dark_palette('cyan', 3, reverse = True, as_cmap=True)\n",
    "\n",
    "plt.scatter(distance, duration, c=distance, cmap= cmap) # it looks like a 10*sqrt(x) power distribution\n",
    "plt.scatter(distance, duration_hat, color='red', s=8)\n",
    "\n",
    "font = {'color': 'red', 'size': 14, 'family': 'Tahoma'}\n",
    "plt.text(31, 40, 'y = 10 * sqrt(x)', fontdict = font) \n",
    "plt.xlim(0, 40)\n",
    "plt.xlabel('Distance', fontsize = 14, weight='bold')\n",
    "plt.ylabel('Duration (Minutes)', fontsize = 14, weight='bold')\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.title('Average Trip Duration and Distance per Origin-Destination Pair', fontsize = 16, color='navy')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>duration_min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin_taz</th>\n",
       "      <th>destination_taz</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <th>18</th>\n",
       "      <td>28.071694</td>\n",
       "      <td>53.168548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">14</th>\n",
       "      <th>17</th>\n",
       "      <td>27.880822</td>\n",
       "      <td>57.425156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>29.943150</td>\n",
       "      <td>62.517062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">16</th>\n",
       "      <th>17</th>\n",
       "      <td>31.092205</td>\n",
       "      <td>62.504593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>28.329221</td>\n",
       "      <td>45.567876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">17</th>\n",
       "      <th>14</th>\n",
       "      <td>27.487630</td>\n",
       "      <td>57.879287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>39.435263</td>\n",
       "      <td>81.276901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7C</th>\n",
       "      <td>25.229773</td>\n",
       "      <td>57.257576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>25.500478</td>\n",
       "      <td>46.694663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7A</th>\n",
       "      <th>17</th>\n",
       "      <td>29.569512</td>\n",
       "      <td>59.605488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7C</th>\n",
       "      <th>17</th>\n",
       "      <td>26.983182</td>\n",
       "      <td>58.360985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">8</th>\n",
       "      <th>17</th>\n",
       "      <td>25.761857</td>\n",
       "      <td>55.740833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>29.537605</td>\n",
       "      <td>59.405964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">9</th>\n",
       "      <th>17</th>\n",
       "      <td>27.438142</td>\n",
       "      <td>49.989370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>36.920512</td>\n",
       "      <td>65.957674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            trip_distance  duration_min\n",
       "origin_taz destination_taz                             \n",
       "13         18                   28.071694     53.168548\n",
       "14         17                   27.880822     57.425156\n",
       "           18                   29.943150     62.517062\n",
       "16         17                   31.092205     62.504593\n",
       "           18                   28.329221     45.567876\n",
       "17         14                   27.487630     57.879287\n",
       "           16                   39.435263     81.276901\n",
       "           7C                   25.229773     57.257576\n",
       "           9                    25.500478     46.694663\n",
       "7A         17                   29.569512     59.605488\n",
       "7C         17                   26.983182     58.360985\n",
       "8          17                   25.761857     55.740833\n",
       "           18                   29.537605     59.405964\n",
       "9          17                   27.438142     49.989370\n",
       "           18                   36.920512     65.957674"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_plt6[df_plt6.trip_distance > 25] #origin and destination pairs whose distance between them is greater than 25 miles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of the average speed per trip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.089890e+07\n",
       "mean     1.350711e+01\n",
       "std      1.175693e+01\n",
       "min      0.000000e+00\n",
       "25%      8.409836e+00\n",
       "50%      1.144876e+01\n",
       "75%      1.626667e+01\n",
       "max      2.254800e+04\n",
       "Name: trip_mph_avg, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Note: this uses processed data (24904 (0.08%) entries with null or zero in duration resulted in null value for speed)\n",
    "df_viz.trip_mph_avg.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1313433, 10564782,  9861693,  4248216,  2152654,  1281911,\n",
       "          767746,   434298,   196149,    61858]),\n",
       " array([  0.,   5.,  10.,  15.,  20.,  25.,  30.,  35.,  40.,  45.,  50.]))"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq, bins = np.histogram(df_viz.trip_mph_avg, bins=10, range=(0, 50))\n",
    "freq, bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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1aN26tcZ6Bw4cwNChQ/HOO+/gxx9/1N1OkkF78CAfR4/eAgAUFZXg1q1HWLEiCQcOXMeG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\nIySoEEIIIUSPkKBCCCGEED3iPxHXklGm7/P1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dc66410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1)\n",
    "\n",
    "formatter = FuncFormatter(millions_format)\n",
    "\n",
    "ax.hist(df_viz.trip_mph_avg.dropna(), bins)\n",
    "ax.yaxis.set_major_formatter(formatter)\n",
    "\n",
    "plt.xlabel('Average Speed (mph)', fontsize=14, weight='bold')\n",
    "plt.ylabel('Counts', fontsize=14, weight='bold')\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.title('Distribution of Average Speed', color='navy', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hour\n",
       "0   14.3002\n",
       "1   15.3747\n",
       "2   16.1274\n",
       "3   16.6125\n",
       "4   19.5019\n",
       "Name: trip_mph_avg, dtype: float64"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Median trip average speed during weekdays:\n",
    "df_plt7 = df_viz[df_viz.weekday < 5].groupby('hour')['trip_mph_avg'].median()\n",
    "df_plt7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JFnv2rAULCwNs2HBNXrh07VpDPpBAzjU6OXbsGIgZM05h4cJzePEiCba2pli8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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ee9abd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot Peak Hour Median Trip Average Speed During Weekdays:\n",
    "\n",
    "plt.figure(figsize =(14, 6))\n",
    "kwargs = {'fontsize': 12, 'ha':'center', 'va': 'top', 'color': 'navy', 'weight': 'bold'}\n",
    "\n",
    "#weekdays only: rush hour traffic 7-9 + 16-18\n",
    "ax = df_plt7.plot(marker = 'o', color = 'navy')\n",
    "\n",
    "for x, y in zip(df_plt7.index, df_plt7.values):\n",
    "    ax.annotate('{:.0f}'.format(y), xy=(x, y), xytext= (0, 24), textcoords='offset points', **kwargs)\n",
    "\n",
    "ax.set_axis_bgcolor('#F9F9F9')\n",
    "ax.get_yaxis().set_ticks([]) #hide tick labels on y-axis\n",
    "plt.fill([7,9,9,7], [0,0,30,30], 'cyan', alpha=0.2)\n",
    "plt.fill([16,18,18,16], [0,0,30,30], 'cyan', alpha=0.2)\n",
    "plt.xticks(range(24))\n",
    "plt.xlabel('Hour', fontsize=14)\n",
    "plt.ylabel('Trip Average Speed', fontsize=14)\n",
    "plt.ylim(5, 30)\n",
    "plt.xlim(-0.5, 23.5)\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.title('Weekday Average Speed per Hour of the Day - Highlight for Peak Traffic', fontsize = 16, color='navy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def is_peak_hour(x):\n",
    "    \"\"\" \n",
    "    Function that takes an array(x) with two integers representing \n",
    "    hour of the day and weekday, respectively, and\n",
    "    returns 1 if it's peak hour as defined, 0 otherwise.\n",
    "    \"\"\"\n",
    "    return 1 if x[0] in (7,8,9,16,17,18) and x[1] < 5 else 0 #total peak hour periods = 6h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_viz['peak_hour'] = pd.Series(map(lambda x: is_peak_hour(x), df_viz.loc[:, ['hour', 'weekday']].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>origin_taz</th>\n",
       "      <th>destination_taz</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>pu_date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>trip_mph_avg</th>\n",
       "      <th>est_revenue</th>\n",
       "      <th>peak_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>252581</td>\n",
       "      <td>7C</td>\n",
       "      <td>6A</td>\n",
       "      <td>4.25</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>15.183333</td>\n",
       "      <td>16.794731</td>\n",
       "      <td>15.301667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>252582</td>\n",
       "      <td>7B</td>\n",
       "      <td>15</td>\n",
       "      <td>10.17</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>34.083333</td>\n",
       "      <td>17.903178</td>\n",
       "      <td>32.276667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>252583</td>\n",
       "      <td>11</td>\n",
       "      <td>2A</td>\n",
       "      <td>4.02</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>14.105263</td>\n",
       "      <td>15.570000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>252584</td>\n",
       "      <td>3B</td>\n",
       "      <td>4A</td>\n",
       "      <td>1.46</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6.533333</td>\n",
       "      <td>13.408163</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>252585</td>\n",
       "      <td>2A</td>\n",
       "      <td>10</td>\n",
       "      <td>8.31</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>26.283333</td>\n",
       "      <td>18.970197</td>\n",
       "      <td>26.291667</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id origin_taz destination_taz  trip_distance    pu_date  year  month  \\\n",
       "0  252581         7C              6A           4.25 2014-09-01  2014      9   \n",
       "1  252582         7B              15          10.17 2014-09-01  2014      9   \n",
       "2  252583         11              2A           4.02 2014-09-01  2014      9   \n",
       "3  252584         3B              4A           1.46 2014-09-01  2014      9   \n",
       "4  252585         2A              10           8.31 2014-09-01  2014      9   \n",
       "\n",
       "   day  hour  weekday  duration_min  trip_mph_avg  est_revenue  peak_hour  \n",
       "0    1     9        0     15.183333     16.794731    15.301667        1.0  \n",
       "1    1    18        0     34.083333     17.903178    32.276667        1.0  \n",
       "2    1    17        0     17.100000     14.105263    15.570000        1.0  \n",
       "3    1    13        0      6.533333     13.408163     8.000000        0.0  \n",
       "4    1    14        0     26.283333     18.970197    26.291667        0.0  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_viz.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/paulaalves/anaconda/lib/python2.7/site-packages/matplotlib/axes/_axes.py:504: UserWarning: You have mixed positional and keyword arguments, some input will be discarded.\n",
      "  warnings.warn(\"You have mixed positional and keyword \"\n"
     ]
    },
    {
     "data": {
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NHeutfv2q68cffzdN2/749Tx4cB1Vq/aShg7drqiolMNLH9WtWxU9fGhM8dyX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a99a550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the total number of trips per month during peak hours and off-peak hours.\n",
    "#Count for peak hours must represent more than 25% (6h) of the total rides to be more significant than non-peak hours.\n",
    "plt.figure(figsize = (14, 6))\n",
    "formatter = FuncFormatter(millions_format)\n",
    "\n",
    "#to add labels and operate on data, use pivot instead of countplot directly with hue.\n",
    "ax = sns.countplot(df_viz['month'], hue = df_viz['peak_hour'])\n",
    "ax.set_xticklabels(month_labels)\n",
    "ax.yaxis.set_major_formatter(formatter)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels() #will cause the warning msg that can be ignored \n",
    "plt.legend(handles, labels=['Peak', 'Off Peak'], loc='best', fontsize=13)\n",
    "plt.tick_params(labelsize=13)\n",
    "plt.xlabel('Month (2014 - 2015)', fontsize = 15, weight='bold')\n",
    "plt.ylabel('Total Trips', fontsize = 15, weight='bold')\n",
    "plt.title('Demand of Rides per Month - Peak versus Off-Peak Hours', fontsize = 17, color='navy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Modeling (to be continued...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join Weather Data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Weather Data Key (Central Park, NYC Station):\n",
    "#PRCP = precipitation in inches\n",
    "#SNWD = snow depth in inches\n",
    "#SNOW = snowfall in inches\n",
    "#TMAX = max temperature in F (minimum of 2F for entire period from 2014 to 2015)\n",
    "#TMIN = min temperature in F (maximum of 97F for the entire period from 2014 to 2015)\n",
    "#AWND = average daily wind speed in MPH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Refer to data source. Use integrated weather data to forecast demand for rides.\n",
    "weather = pd.read_csv('Data/weather.csv', na_values = -9999, parse_dates = ['DATE'], \\\n",
    "                      usecols = [u'DATE', u'PRCP', u'SNWD', u'SNOW', u'TMAX', u'TMIN', u'AWND'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pu_date</th>\n",
       "      <th>prcp</th>\n",
       "      <th>snwd</th>\n",
       "      <th>snow</th>\n",
       "      <th>tmax</th>\n",
       "      <th>tmin</th>\n",
       "      <th>awnd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>33</td>\n",
       "      <td>18</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>0.29</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.3</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-05</td>\n",
       "      <td>0.14</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>27</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pu_date  prcp  snwd  snow  tmax  tmin  awnd\n",
       "0 2014-01-01  0.00   0.0   0.0    33    24   5.6\n",
       "1 2014-01-02  0.33   0.0   3.1    33    18  12.3\n",
       "2 2014-01-03  0.29   5.9   3.3    18     9  10.5\n",
       "3 2014-01-04  0.00   5.9   0.0    29     8   4.9\n",
       "4 2014-01-05  0.14   3.9   0.0    40    27   3.8"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.columns = ['pu_date', 'prcp', 'snwd', 'snow', 'tmax', 'tmin', 'awnd']\n",
    "weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24    7.6\n",
       "25    NaN\n",
       "26    7.8\n",
       "Name: awnd, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There are some nan missing values for wind speed:\n",
    "weather.awnd[24:27]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Merge weather data\n",
    "#avoiding unncessary sorting by key improves perfomance\n",
    "df_merged = pd.merge(df_uber, weather, on='pu_date', how='left', sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>origin_taz</th>\n",
       "      <th>destination_taz</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>pu_date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>trip_mph_avg</th>\n",
       "      <th>est_revenue</th>\n",
       "      <th>prcp</th>\n",
       "      <th>snwd</th>\n",
       "      <th>snow</th>\n",
       "      <th>tmax</th>\n",
       "      <th>tmin</th>\n",
       "      <th>awnd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>252581</td>\n",
       "      <td>7C</td>\n",
       "      <td>6A</td>\n",
       "      <td>4.25</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>15.183333</td>\n",
       "      <td>16.794731</td>\n",
       "      <td>15.301667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88</td>\n",
       "      <td>75</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>252582</td>\n",
       "      <td>7B</td>\n",
       "      <td>15</td>\n",
       "      <td>10.17</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>34.083333</td>\n",
       "      <td>17.903178</td>\n",
       "      <td>32.276667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88</td>\n",
       "      <td>75</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>252583</td>\n",
       "      <td>11</td>\n",
       "      <td>2A</td>\n",
       "      <td>4.02</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>14.105263</td>\n",
       "      <td>15.570000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88</td>\n",
       "      <td>75</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>252584</td>\n",
       "      <td>3B</td>\n",
       "      <td>4A</td>\n",
       "      <td>1.46</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6.533333</td>\n",
       "      <td>13.408163</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88</td>\n",
       "      <td>75</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>252585</td>\n",
       "      <td>2A</td>\n",
       "      <td>10</td>\n",
       "      <td>8.31</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>26.283333</td>\n",
       "      <td>18.970197</td>\n",
       "      <td>26.291667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88</td>\n",
       "      <td>75</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id origin_taz destination_taz  trip_distance    pu_date  year  month  \\\n",
       "0  252581         7C              6A           4.25 2014-09-01  2014      9   \n",
       "1  252582         7B              15          10.17 2014-09-01  2014      9   \n",
       "2  252583         11              2A           4.02 2014-09-01  2014      9   \n",
       "3  252584         3B              4A           1.46 2014-09-01  2014      9   \n",
       "4  252585         2A              10           8.31 2014-09-01  2014      9   \n",
       "\n",
       "   day  hour  weekday  duration_min  trip_mph_avg  est_revenue  prcp  snwd  \\\n",
       "0    1     9        0     15.183333     16.794731    15.301667   0.0   0.0   \n",
       "1    1    18        0     34.083333     17.903178    32.276667   0.0   0.0   \n",
       "2    1    17        0     17.100000     14.105263    15.570000   0.0   0.0   \n",
       "3    1    13        0      6.533333     13.408163     8.000000   0.0   0.0   \n",
       "4    1    14        0     26.283333     18.970197    26.291667   0.0   0.0   \n",
       "\n",
       "   snow  tmax  tmin  awnd  \n",
       "0   0.0    88    75   2.9  \n",
       "1   0.0    88    75   2.9  \n",
       "2   0.0    88    75   2.9  \n",
       "3   0.0    88    75   2.9  \n",
       "4   0.0    88    75   2.9  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 30925738 entries, 0 to 30925737\n",
      "Data columns (total 19 columns):\n",
      "id                 int64\n",
      "origin_taz         object\n",
      "destination_taz    object\n",
      "trip_distance      float64\n",
      "pu_date            datetime64[ns]\n",
      "year               int64\n",
      "month              int64\n",
      "day                int64\n",
      "hour               int64\n",
      "weekday            int64\n",
      "duration_min       float64\n",
      "trip_mph_avg       float64\n",
      "est_revenue        float64\n",
      "prcp               float64\n",
      "snwd               float64\n",
      "snow               float64\n",
      "tmax               int64\n",
      "tmin               int64\n",
      "awnd               float64\n",
      "dtypes: datetime64[ns](1), float64(8), int64(8), object(2)\n",
      "memory usage: 4.6+ GB\n"
     ]
    }
   ],
   "source": [
    "df_merged.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Show all dataframes in the environment:\n",
    "#%psearch df*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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